A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges

Chenyu Li; Danfeng Hong; Hongruixuan Chen; Jie Deng; Jocelyn Chanussot; Lei Ding; Lorenzo Bruzzone; Maofan Zhao; Naoto Yokoya

REVIEW 3 major objections 6 minor 187 references

A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Sample-efficient change detection can reach near-fully-supervised accuracy with only 5% of training labels, and self-supervised pretraining with fine-tuning can slightly exceed full supervision.

desk verdict A useful survey of sample-efficient change detection whose quantitative accuracy comparison in Section III-E is internally inconsistent and needs correction before the numbers can be trusted. read the letter →

arxiv 2502.02835 v1 pith:443LWZOK submitted 2025-02-05 cs.CV

Lei Ding , Danfeng Hong , Maofan Zhao , Hongruixuan Chen , Chenyu Li , Jie Deng , Naoto Yokoya , Lorenzo Bruzzone

show 1 more author

Jocelyn Chanussot

This is my paper · ORCID

classification cs.CV

keywords changedetectionremotesensingsample-efficientlearningsemi-supervisedweaklysupervisedself-supervisedunsupervisedvisualfoundationmodels

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to establish that sample-efficient change detection in remote sensing can be organized into four supervision paradigms, and that accuracy tracks supervision strength. It argues that semi-supervised methods with only 5% of training labels lose just about 2% F1 on LEVIR and 0.6% on OSCD compared to full supervision, and that self-supervised pretraining with fine-tuning can slightly exceed fully supervised accuracy. The paper also maps the concrete strategies—pseudo-labeling, consistency regularization, contrastive learning, generative models, augmentation, and foundation-model adaptation—that make these results possible. If the accuracy hierarchy holds, practitioners can choose label budgets rationally instead of assuming full supervision is required.

What carries the argument

The organizing device is a supervision-level taxonomy of sample-efficient change detection: semi-supervised CD (few labels plus unlabeled data, including few-shot), weakly supervised CD (coarse labels such as image-level, points, or boxes), self-supervised CD (pretraining on unlabeled data, with or without fine-tuning), and unsupervised CD (no labels). Within that taxonomy, the survey groups concrete strategies—pseudo-labeling, consistency regularization, graph-based propagation, change activation mapping, contrastive learning, masked image modeling, generative representation, augmentation, and external knowledge from foundation models—into a per-paradigm map. The taxonomy does the work of turning a scattered literature into a testable claim about accuracy versus supervision strength.

What would settle it

Run the same semi-supervised, weakly supervised, self-supervised, and unsupervised change detection methods on LEVIR, WHU, and OSCD under one standardized protocol with identical backbones and 5% label budgets; if the reported F1 ordering reverses or the 2% and 0.6% reductions become much larger, the accuracy hierarchy and the sample-efficiency claims do not survive.

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Extended reading notes

Core claim

The central claim is that change detection accuracy follows a supervision hierarchy: fully supervised CD and self-supervised CD with fine-tuning lead; semi-supervised CD follows closely; weakly supervised, unsupervised, and self-supervised without fine-tuning trail. The paper collects state-of-the-art numbers from the LEVIR, WHU, and OSCD benchmark datasets to support this ordering, reporting that state-of-the-art semi-supervised methods fall only about 2% in F1 on LEVIR and 0.6% on OSCD when trained with 5% of labels. It also claims that self-supervised methods with fine-tuning marginally surpass full supervision, attributing this to extensive pretraining that uses image contexts as extra supervision. The paper acknowledges that the numbers come from varying experimental settings and says the comparison table is intended solely to provide an intuitive assessment.

Load-bearing premise

The quantitative conclusion assumes that the accuracy numbers collected from different papers are comparable even though the papers use different experimental protocols; the paper itself says the comparison table is intended solely to provide an intuitive assessment.

Editorial extensions

If this is right

If the hierarchy holds, semi-supervised CD with 5% of labels is a practical substitute for full supervision on common benchmarks, cutting annotation cost dramatically.
Self-supervised pretraining followed by fine-tuning appears to give an edge over training from scratch with all labels, especially on small datasets like OSCD where the reported improvement reaches up to 12% F1.
Weakly supervised methods with point labels can exceed image-label methods by more than 30% F1, so choosing the right weak label type matters as much as choosing the algorithm.
Fully label-free and zero-shot CD still trails by roughly 30% F1 on very-high-resolution data, so the remaining bottleneck is unsupervised and unseen-change detection, not semi-supervision.
The accuracy gap across datasets is tied to resolution and change-sample richness, meaning high-resolution datasets are where sample-efficient methods should be tested first.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper, the supervision hierarchy can be treated as a prediction and tested by a standardized benchmark that controls backbone and protocol; no such benchmark currently exists.
The survey's taxonomy suggests that combining strategies across paradigms—self-supervised pretraining plus semi-supervised fine-tuning plus augmentation—should close most of the remaining gap to full supervision.
A natural extension is to treat foundation-model-based zero-shot CD as a fifth paradigm and measure its scaling with model size, since its current 24.5% F1 on LEVIR leaves substantial room for improvement.
The 0.6% OSCD claim is the most fragile number in the paper because OSCD has little training data and high variance; re-running the method under multiple random seeds would show how stable that figure is.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take on arXiv:2502.02835. It's a genuinely useful survey: the taxonomy of four supervision paradigms is clear, the strategy table is a handy reference, and the coverage of recent foundation-model work is current. If you need a map of this subfield, this is a good one to hand a new student.

The soft spot is real and lands in Section III-E. The text says SOTA SMCD with 5% labels loses about 2% F1 on LEVIR and 0.6% on OSCD. The LEVIR number roughly holds (92.06 vs 90.01), but the OSCD number is off by an order of magnitude: Table II's best FSCD on OSCD is 59.20 and best SMCD at 5% is 54.07, a 5.13-point gap. The table itself warns that experimental settings vary across studies and is meant only for intuitive assessment, yet the prose uses those numbers to make precise reduction claims and assert an accuracy hierarchy. That inconsistency is load-bearing because the conclusion that sample-efficient methods approach fully supervised accuracy rests on it. The fix is straightforward: either compare under matched protocols or reframe the section as non-comparable best-reported results with appropriate caveats.

The Web of Science statistics in Fig. 1 also lack a search protocol, so they aren't reproducible. Minor, but easy to correct.

I wouldn't hold the absence of a new method against a survey; the taxonomy and coverage are the contribution, and they're competent. Self-citation is high but the authors are central to this subfield, so I don't read that as a problem.

Bottom line: this deserves a serious referee. I'd send it to review and request a revision that fixes the quantitative comparison and softens the claims. After that it would be a solid reference for the community.

Referee Report

3 major / 6 minor

Summary. This paper surveys deep-learning change detection methods under limited supervision. It organizes the field into three tasks (binary CD, multi-class/semantic CD, and time-series CD) and four sample-efficient learning paradigms (semi-supervised, weakly supervised, self-supervised, and unsupervised). For each paradigm it reviews representative strategies, presents a comparison table of reported accuracies on LEVIR, WHU, and OSCD, and concludes that sample-efficient methods, especially semi-supervised and self-supervised with fine-tuning, approach fully supervised accuracy while unsupervised and zero-shot methods remain far behind. It closes with challenges and future directions. The paper is primarily a literature organization contribution rather than a technical derivation, and its main quantitative evidence appears in Section III-E.

Significance. The taxonomy is useful and largely faithful to the literature: the four-way division of sample-efficient CD is intuitive, Table I provides a practical synthesis of strategies, and the coverage of recent vision-foundation-model methods is timely. The qualitative organization should help researchers entering the area. However, the quantitative comparison in Section III-E is currently the weak point: the prose draws precise conclusions from numbers that the paper itself declares non-comparable, and at least one of the specific reduction claims is contradicted by the paper's own tables. If these issues are corrected, the survey would be a solid contribution; as it stands, the central quantitative claim is not internally supported.

major comments (3)

[Section III-E, Tables II and III] The sentence stating that with 5% training data the SOTA SMCD methods see only a 0.6% F1 reduction on OSCD is contradicted by the paper's own Table III: the best FSCD F1 is 59.20 ([26]) and the best SMCD (5%) F1 is 54.07 ([53]), a 5.13-point gap, or 8.7% relative reduction. On LEVIR the corresponding gap is 92.06 ([3]) to 90.01 ([48]), 2.05 points. Because the 0.6% figure is off by roughly an order of magnitude and this sentence is the main quantitative support for the survey's central conclusion that sample-efficient methods approach fully supervised accuracy, the numbers must be corrected or the claim reframed.
[Section III-E, Table II preamble] The table explicitly states that 'the experimental settings exhibit variations across different studies in the literature' and that it is 'intended solely to provide an intuitive assessment,' yet the following prose computes precise percentage differences (2%, 0.6%, 12%) and asserts a strict accuracy hierarchy from these non-matched numbers. These precise statements are not supported by the evidence as presented. The authors should either perform a controlled comparison under a common protocol or explicitly disclaim quantitative comparisons and present only best-reported values with no precise percentage claims.
[Section III-E, Tables II and III] The asserted supervision hierarchy is internally inconsistent. The text states that 'SMCD achieves the highest accuracy among sample-efficient CD approaches,' but Table III's OSCD column lists the best SSCD without fine-tuning at 55.69 ([166]), which is above the best SMCD at 5% labels (54.07, [53]). Likewise, the claim that 'the SSCD with FT marginally surpasses FSCD' is not true on OSCD, where Table II reports TD-SSCD at 72.11 ([89]) versus the best FSCD at 59.20 ([26]), a 12.91-point gap. The hierarchy claims need to be qualified by dataset and by whether fine-tuning is used.

minor comments (6)

[Section III-E] The phrase 'Regarding image label-supervised SMCD' should read 'WSCD' (or should be rephrased), since the methods [67] and [69] are weakly supervised methods that use image-level labels.
[References] Reference [132] is missing author names in the bibliography; the entry should be completed.
[Fig. 4 and Eq. (4)] Fig. 4 contains the typo 'origninal' (should be 'original'), and Eq. (4) contains 'sof tmax' (should be 'softmax').
[Section I, Fig. 1] The Web of Science statistics underlying Fig. 1 should report the search date, exact query strings, and inclusion and exclusion criteria so that the counts are reproducible.
[Section III-E] The sentence 'Based on the number of change instances detailed in Table II' should refer to Table III, where the change-instance counts actually appear.
[Table III] The LEVIR row of Table III appears to omit the SSCD (w/o FT) entry, making the column alignment ambiguous; an explicit dash should be added for consistency with the WHU row.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's four-paradigm taxonomy and literature-based accuracy comparison summarize external results rather than deriving conclusions from their own inputs.

full rationale

This is a survey paper whose central content is a taxonomy of sample-efficient change detection methods (SMCD, WSCD, SSCD, UCD) and a literature-based comparison. The taxonomy is defined by supervision level, not fitted to any accuracy outcome. The accuracy hierarchy in Section III-E is a summary of reported F1 numbers in Tables II-III, which come from external benchmarks; even where the authors' own methods appear (e.g., SAM-CD [4]), the reported scores are results on public datasets and are not used to define the taxonomy. The specific claim that 5%-labeled SMCD loses only 0.6% F1 on OSCD is inconsistent with the paper's own Table III (59.20 - 54.07 = 5.13 points), and the table itself warns that experimental settings vary across studies; this is an internal-consistency or correctness problem, not a circularity. Nothing in the paper defines a quantity in terms of the target conclusion, fits a parameter and renames it as a prediction, or imports a load-bearing uniqueness theorem from the authors' prior work. Self-citations appear as representative literature entries, but no central claim reduces to those citations. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The survey introduces no free parameters, no new axioms beyond the standard assumption that cited numbers are correct, and no invented entities. Its claims are descriptive and taxonomic; the only empirical synthesis is the cross-paper accuracy comparison, which rests on the comparability assumption above.

assumptions (1)

domain assumption The state-of-the-art accuracy numbers in Table II are faithfully transcribed from the cited papers and are comparable across studies despite differing experimental settings.
The quantitative conclusions in Section III-E (accuracy hierarchy, percentage reductions such as the '0.6%' claim) depend on the comparability of these numbers. The table itself warns of significant variations, so this assumption is weak and load-bearing.

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Cite this review

Pith. "Pith review of A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges." pith.science (2026). https://pith.science/paper/443LWZOK

@misc{pith2026250202835,
  author       = {Pith},
  title        = {Pith review of: A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/443LWZOK}},
  note         = {Machine review of arXiv:2502.02835}
}

read the original abstract

In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the applicational context. For example, the collected RSIs can be time-series observations, and more informative results are required to indicate the time of change or the specific change category. Moreover, training a Deep Neural Network (DNN) requires a massive amount of training samples, whereas in many cases these samples are difficult to collect. To address these challenges, various specific CD methods have been developed considering different application scenarios and training resources. Additionally, recent advancements in image generation, self-supervision, and visual foundation models (VFMs) have opened up new approaches to address the 'data-hungry' issue of DL-based CD. The development of these methods in broader application scenarios requires further investigation and discussion. Therefore, this article summarizes the literature methods for different CD tasks and the available strategies and techniques to train and deploy DL-based CD methods in sample-limited scenarios. We expect that this survey can provide new insights and inspiration for researchers in this field to develop more effective CD methods that can be applied in a wider range of contexts.

Figures

Figures reproduced from arXiv: 2502.02835 by the authors.

**Figure 2.** A comparison between (a) BCD, (b) MCD/SCD, and (c) TSCD. The color regions in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Comparison of annotation and data volume in different CD learning paradigms. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Consistency regularization for WSCD [45]. Random perturbations are applied to the change representations, and a consistency loss is calculated between the origninal and perturbed CD results to improve the robustness of CD models. Among auxiliary regularization-based ap…

**Figure 5.** Figure 5: Refining CAM for SMCD within a teacher-student [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: A simplified paradigm of contrastive learning for SSCD [ [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: The paradigm of semantic change augmentation in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: The paradigm of leveraging VFM for CD in [ [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

187 extracted references · 67 canonical work pages

[26]

Multitask learning for large-scale semantic change detection,

R. C. Daudt, B. Le Saux, A. Boulch, and Y . Gousseau, “Multitask learning for large-scale semantic change detection,” Computer Vision and Image Understanding , vol. 187, p. 102783, 2019

2019
[53]

Ecps: Cross pseudo supervision based on ensemble learning for semi-supervised remote sensing change detection,

Y . Yang, X. Tang, J. Ma, X. Zhang, S. Pei, and L. Jiao, “Ecps: Cross pseudo supervision based on ensemble learning for semi-supervised remote sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2024

2024
[3]

Changer: Feature interaction is what you need for change detection,

S. Fang, K. Li, and Z. Li, “Changer: Feature interaction is what you need for change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–11, 2023

2023
[48]

Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images,

Y . Zuo, L. Li, X. Liu, Z. Gao, L. Jiao, F. Liu, and S. Yang, “Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images,” vol. 62, pp. 1–15
[166]

Domain Knowledge-Guided Self- Supervised Change Detection for Remote Sensing Images,

L. Yan, J. Yang, and J. Wang, “Domain Knowledge-Guided Self- Supervised Change Detection for Remote Sensing Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 4167–4179

work page
[89]

TD-SSCD: A Novel Network by Fusing Temporal and Differential Information for Self-Supervised Remote Sensing Image Change Detection,

Y . Qu, J. Li, X. Huang, and D. Wen, “TD-SSCD: A Novel Network by Fusing Temporal and Differential Information for Self-Supervised Remote Sensing Image Change Detection,” vol. 61, pp. 1–15
[1]

Spectralgpt: Spectral remote sensing foundation model,

D. Hong, B. Zhang, X. Li, Y . Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia, A. Plaza, P. Gamba, J. A. Benediktsson, and J. Chanussot, “Spectralgpt: Spectral remote sensing foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 8, pp. 5227–5244, 2024

2024
[2]

Remote sensing image change detection with transformers,

H. Chen, Z. Qi, and Z. Shi, “Remote sensing image change detection with transformers,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2021

2021

Show all 187 references

[4]

Adapting segment anything model for change detection in hr remote sensing images,

L. Ding, K. Zhu, D. Peng, H. Tang, K. Yang, and L. Bruzzone, “Adapting segment anything model for change detection in hr remote sensing images,” IEEE Transactions on Geoscience and Remote Sens- ing, vol. 62, pp. 1–11, 2024

2024
[5]

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges,

W. Shi, M. Zhang, R. Zhang, S. Chen, and Z. Zhan, “Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges,” Remote Sensing, vol. 12, no. 10, p. 1688
[6]

S2looking: A satellite side-looking dataset for building change detection,

L. Shen, Y . Lu, H. Chen, H. Wei, D. Xie, J. Yue, R. Chen, S. Lv, and B. Jiang, “S2looking: A satellite side-looking dataset for building change detection,” Remote Sensing, vol. 13, no. 24, p. 5094, 2021

2021
[7]

Cheng, Y

G. Cheng, Y . Huang, X. Li, S. Lyu, Z. Xu, Q. Zhao, and S. Xiang. Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review. [Online]. Available: http://arxiv.org/abs/2305.05813

arXiv
[8]

End-to-end change detection for high resolution satellite images using improved unet++,

D. Peng, Y . Zhang, and H. Guan, “End-to-end change detection for high resolution satellite images using improved unet++,” Remote Sensing , vol. 11, no. 11, p. 1382, 2019

2019
[9]

Fully convolutional siamese networks for change detection,

R. C. Daudt, B. Le Saux, and A. Boulch, “Fully convolutional siamese networks for change detection,” in 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018, pp. 4063–4067

2018
[10]

A feature difference convolutional neural network-based change detection method,

M. Zhang and W. Shi, “A feature difference convolutional neural network-based change detection method,” IEEE Transactions on Geo- science and Remote Sensing , vol. 58, no. 10, pp. 7232–7246, 2020

2020
[11]

High-resolution triplet network with dynamic multiscale feature for change detection on satellite images,

X. Hou, Y . Bai, Y . Li, C. Shang, and Q. Shen, “High-resolution triplet network with dynamic multiscale feature for change detection on satellite images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 177, pp. 103–115, 2021

2021
[12]

Remote sensing change detection via temporal feature interaction and guided refinement,

Z. Li, C. Tang, L. Wang, and A. Y . Zomaya, “Remote sensing change detection via temporal feature interaction and guided refinement,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022. GRSM 16

2022
[13]

Scdnet: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery,

D. Peng, L. Bruzzone, Y . Zhang, H. Guan, and P. He, “Scdnet: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery,”International Journal of Applied Earth Observation and Geoinformation , vol. 103, p. 102465, 2021

2021
[14]

Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images,

J. Chen, Z. Yuan, J. Peng, L. Chen, H. Huang, J. Zhu, Y . Liu, and H. Li, “Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , ...

2020
[15]

A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection,

Q. Shi, M. Liu, S. Li, X. Liu, F. Wang, and L. Zhang, “A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection,” IEEE transactions on geoscience and remote sensing , vol. 60, pp. 1–16, 2021

2021
[16]

Change detection in multisource vhr images via deep siamese convolutional multiple- layers recurrent neural network,

H. Chen, C. Wu, B. Du, L. Zhang, and L. Wang, “Change detection in multisource vhr images via deep siamese convolutional multiple- layers recurrent neural network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 58, no. 4, pp. 2848–2864, 2019

2019
[17]

A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images,

J. Wu, B. Li, Y . Qin, W. Ni, H. Zhang, R. Fu, and Y . Sun, “A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images,” International Journal of Applied Earth Observation and Geoinformation , vol. 105, p. 102615, 2021

2021
[18]

An image is worth 16x16 words: Transformers for image recognition at scale,

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

2010 arXiv
[19]

Casformer: Cascaded transformers for fusion-aware computational hyperspectral imaging,

C. Li, B. Zhang, D. Hong, J. Zhou, G. Vivone, S. Li, and J. Chanussot, “Casformer: Cascaded transformers for fusion-aware computational hyperspectral imaging,” Information Fusion, vol. 108, p. 102408, 2024

2024
[20]

Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,

L. Ding, D. Lin, S. Lin, J. Zhang, X. Cui, Y . Wang, H. Tang, and L. Bruzzone, “Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022

2022
[21]

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks,

D. Hong, B. Zhang, H. Li, Y . Li, J. Yao, C. Li, M. Werner, J. Chanussot, A. Zipf, and X. X. Zhu, “Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks,” Remote Sensing of En- vi...

2023
[22]

A transformer-based siamese network and an open optical dataset for semantic change detection of remote sensing images,

P. Yuan, Q. Zhao, X. Zhao, X. Wang, X. Long, and Y . Zheng, “A transformer-based siamese network and an open optical dataset for semantic change detection of remote sensing images,” International Journal of Digital Earth , vol. 15, no. 1, pp. 1506–1525, 2022

2022
[23]

Remote sensing change detection with transformers trained from scratch,

M. Noman, M. Fiaz, H. Cholakkal, S. Narayan, R. M. Anwer, S. Khan, and F. S. Khan, “Remote sensing change detection with transformers trained from scratch,” IEEE Transactions on Geoscience and Remote Sensing, 2024

2024
[24]

Relation changes matter: Cross-temporal difference transformer for change detection in remote sensing images,

K. Zhang, X. Zhao, F. Zhang, L. Ding, J. Sun, and L. Bruzzone, “Relation changes matter: Cross-temporal difference transformer for change detection in remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[25]

The time variable in data fusion: A change detection perspective,

F. Bovolo and L. Bruzzone, “The time variable in data fusion: A change detection perspective,” IEEE Geoscience and Remote Sensing Magazine, vol. 3, no. 3, pp. 8–26, 2015

2015
[27]

Asymmetric siamese networks for semantic change detection in aerial images,

K. Yang, G.-S. Xia, Z. Liu, B. Du, W. Yang, M. Pelillo, and L. Zhang, “Asymmetric siamese networks for semantic change detection in aerial images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–18, 2021

2021
[28]

Digital change detection techniques using remotely-sensed data,

A. Singh, “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sensing , vol. 10, no. 6, pp. 989–1003, 1989

1989
[29]

An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images,

L. Bruzzone and S. B. Serpico, “An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images,” IEEE transactions on geoscience and remote sensing , vol. 35, no. 4, pp. 858–867, 1997

1997
[30]

A post-classification change detection method based on iterative slow feature analysis and bayesian soft fusion,

C. Wu, B. Du, X. Cui, and L. Zhang, “A post-classification change detection method based on iterative slow feature analysis and bayesian soft fusion,” Remote Sensing of Environment , vol. 199, pp. 241–255, 2017

2017
[31]

Learning spectral-spatial- temporal features via a recurrent convolutional neural network for change detection in multispectral imagery,

L. Mou, L. Bruzzone, and X. X. Zhu, “Learning spectral-spatial- temporal features via a recurrent convolutional neural network for change detection in multispectral imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 2, pp. 924–935, 2018

2018
[32]

Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery,

Q. Zhu, X. Guo, W. Deng, S. Shi, Q. Guan, Y . Zhong, L. Zhang, and D. Li, “Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 184, pp. 63–78
[33]

Changemask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection,

Z. Zheng, Y . Zhong, S. Tian, A. Ma, and L. Zhang, “Changemask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 183, pp. 228–239, 2022

2022
[34]

Joint spatio-temporal modeling for semantic change detection in remote sensing images,

L. Ding, J. Zhang, H. Guo, K. Zhang, B. Liu, and L. Bruzzone, “Joint spatio-temporal modeling for semantic change detection in remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, 2024

2024
[35]

SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis,

W. Li, P. Ma, H. Wang, and C. Fang, “SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–16
[36]

Automated attribution of forest disturbance types from remote sensing data: A synthesis,

A. T. Stahl, R. Andrus, J. A. Hicke, A. T. Hudak, B. C. Bright, and A. J. Meddens, “Automated attribution of forest disturbance types from remote sensing data: A synthesis,” Remote Sensing of Environment, vol. 285, p. 113416
[37]

Using An Attention-Based LSTM Encoder–Decoder Network for Near Real-Time Disturbance Detection,

Y . Yuan, L. Lin, L.-Z. Huo, Y .-L. Kong, Z.-G. Zhou, B. Wu, and Y . Jia, “Using An Attention-Based LSTM Encoder–Decoder Network for Near Real-Time Disturbance Detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 1819– 1832
[38]

Time-series land cover change detection using deep learning-based temporal semantic segmentation,

H. He, J. Yan, D. Liang, Z. Sun, J. Li, and L. Wang, “Time-series land cover change detection using deep learning-based temporal semantic segmentation,” Remote Sensing of Environment , vol. 305, p. 114101
[39]

Deep Learning for Land Cover Change Detection,

O. Sefrin, F. M. Riese, and S. Keller, “Deep Learning for Land Cover Change Detection,” Remote Sensing, vol. 13, no. 1, p. 78
[40]

Change Detection in Image Time-Series Using Unsupervised LSTM,

S. Saha, F. Bovolo, and L. Bruzzone, “Change Detection in Image Time-Series Using Unsupervised LSTM,” IEEE Geoscience and Re- mote Sensing Letters , vol. 19, pp. 1–5
[41]

SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images,

D. Peng, L. Bruzzone, Y . Zhang, H. Guan, H. Ding, and X. Huang, “SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 7, pp. 5891–5906
[42]

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection,

C. Wu, B. Du, and L. Zhang, “Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 8, pp. 9...

2023
[43]

Bi- temporal semantic reasoning for the semantic change detection in hr remote sensing images,

L. Ding, H. Guo, S. Liu, L. Mou, J. Zhang, and L. Bruzzone, “Bi- temporal semantic reasoning for the semantic change detection in hr remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022

2022
[44]

Hypernet: Self-supervised hyperspectral spatial–spectral feature understanding network for hyperspectral change detection,

M. Hu, C. Wu, and L. Zhang, “Hypernet: Self-supervised hyperspectral spatial–spectral feature understanding network for hyperspectral change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–17, 2022

2022
[45]

W. G. C. Bandara and V . M. Patel. Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. [Online]. Available: http://arxiv.org/abs/2204.08454

arXiv
[46]

Revisiting Weak- to-Strong Consistency in Semi-Supervised Semantic Segmentation,

L. Yang, L. Qi, L. Feng, W. Zhang, and Y . Shi, “Revisiting Weak- to-Strong Consistency in Semi-Supervised Semantic Segmentation,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR). IEEE, pp. 7236–7246

2023
[47]

STCRNet: A Semi-Supervised Network Based on Self-Training and Consistency Regularization for Change Detection in VHR Remote Sensing Images,

L. Wang, M. Zhang, and W. Shi, “STCRNet: A Semi-Supervised Network Based on Self-Training and Consistency Regularization for Change Detection in VHR Remote Sensing Images,” vol. 17, pp. 2272– 2282
[49]

Multi- layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images,

J. Shi, T. Wu, H. Yu, A. K. Qin, G. Jeon, and Y . Lei, “Multi- layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images,” vol. 66, no. 4, p. 140308
[50]

Reliable Contrastive Learning for Semi-Supervised Change Detection in Remote Sensing Images,

J.-X. Wang, T. Li, S.-B. Chen, J. Tang, B. Luo, and R. C. Wilson, “Reliable Contrastive Learning for Semi-Supervised Change Detection in Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13
[51]

Dynamically Up- dated Semi-Supervised Change Detection Network Combining Cross- Supervision and Screening Algorithms,

S. Yuan, R. Zhong, C. Yang, Q. Li, and Y . Dong, “Dynamically Up- dated Semi-Supervised Change Detection Network Combining Cross- Supervision and Screening Algorithms,” vol. 62, pp. 1–14
[52]

Detail enhanced change detection in vhr images using a self-supervised multi-scale GRSM 17 hybrid network,

D. Zheng, Z. Wu, J. Liu, C.-C. Hung, and Z. Wei, “Detail enhanced change detection in vhr images using a self-supervised multi-scale GRSM 17 hybrid network,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2024

2024
[54]

SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model,

L. Kondmann, S. Saha, and X. X. Zhu, “SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model,” vol. 16, pp. 3879–3891
[55]

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels,

Y . Wang, H. Wang, Y . Shen, J. Fei, W. Li, G. Jin, L. Wu, R. Zhao, and X. Le, “Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, pp. 4238–4247

2022
[56]

SemiBuildingChange: A Semi- Supervised High-Resolution Remote Sensing Image Building Change Detection Method With a Pseudo Bitemporal Data Generator,

C. Sun, H. Chen, C. Du, and N. Jing, “SemiBuildingChange: A Semi- Supervised High-Resolution Remote Sensing Image Building Change Detection Method With a Pseudo Bitemporal Data Generator,” vol. 61, pp. 1–19
[57]

Land Cover Change De- tection from High-Resolution Remote Sensing Imagery Using Multi- temporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model,

X. Zhang, W. Shi, Z. Lv, and F. Peng, “Land Cover Change De- tection from High-Resolution Remote Sensing Imagery Using Multi- temporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model,” vol. 11, no. 23, p. 2787
[58]

An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning,

X. Tang, H. Zhang, L. Mou, F. Liu, X. Zhang, X. X. Zhu, and L. Jiao, “An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–15
[59]

Deep col- laborative learning with class-rebalancing for semi-supervised change detection in SAR images,

X. Hou, Y . Bai, Y . Xie, H. Ge, Y . Li, C. Shang, and Q. Shen, “Deep col- laborative learning with class-rebalancing for semi-supervised change detection in SAR images,” vol. 264, p. 110281
[60]

Remote sensing image semantic change detection boosted by semi-supervised contrastive learning of semantic segmentation,

X. Zhang, Y . Yang, L. Ran, L. Chen, K. Wang, L. Yu, P. Wang, and Y . Zhang, “Remote sensing image semantic change detection boosted by semi-supervised contrastive learning of semantic segmentation,” IEEE Transactions on Geoscience and Remote Sensing , 2024

2024
[61]

Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing im- ages,

B. Du, L. Ru, C. Wu, and L. Zhang, “Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing im- ages,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 12, pp. 9976–9992, 2019

2019
[62]

Automatic change detection in synthetic aperture radar images based on pcanet,

F. Gao, J. Dong, B. Li, and Q. Xu, “Automatic change detection in synthetic aperture radar images based on pcanet,” IEEE Geoscience and Remote Sensing Letters , vol. 13, no. 12, pp. 1792–1796, 2016

2016
[63]

Change detection in hyperspec- tral images using recurrent 3d fully convolutional networks,

A. Song, J. Choi, Y . Han, and Y . Kim, “Change detection in hyperspec- tral images using recurrent 3d fully convolutional networks,” Remote Sensing, vol. 10, no. 11, 2018

2018
[64]

Superpixel-based dif- ference representation learning for change detection in multispectral remote sensing images,

M. Gong, T. Zhan, P. Zhang, and Q. Miao, “Superpixel-based dif- ference representation learning for change detection in multispectral remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2658–2673, 2017

2017
[65]

Saliency-guided deep neural networks for sar image change detection,

J. Geng, X. Ma, X. Zhou, and H. Wang, “Saliency-guided deep neural networks for sar image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 10, pp. 7365–7377, 2019

2019
[66]

A multi-scale weakly supervised learning method with adaptive online noise correction for high- resolution change detection of built-up areas,

Y . Cao, X. Huang, and Q. Weng, “A multi-scale weakly supervised learning method with adaptive online noise correction for high- resolution change detection of built-up areas,” Remote Sensing of Environment, vol. 297, p. 113779
[67]

B. Lu, C. Ding, J. Bi, and D. Song. Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference. [Online]. Available: http://arxiv.org/abs/2403.05796

arXiv
[68]

Be- yond Pixel-Level Annotation: Exploring Self-Supervised Learning for Change Detection With Image-Level Supervision,

M. Zhao, X. Hu, L. Zhang, Q. Meng, Y . Chen, and L. Bruzzone, “Be- yond Pixel-Level Annotation: Exploring Self-Supervised Learning for Change Detection With Image-Level Supervision,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16
[69]

A Siamese Network Combining Multiscale Joint Supervision and Improved Con- sistency Regularization for Weakly Supervised Building Change Detec- tion,

Y . Dai, K. Zhao, L. Shen, S. Liu, X. Yan, and Z. Li, “A Siamese Network Combining Multiscale Joint Supervision and Improved Con- sistency Regularization for Weakly Supervised Building Change Detec- tion,” IEEE Journal of Selected Topics in Applied Earth Observations and Remot...
[70]

Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learn- ing,

I. Kalita, S. Karatsiolis, and A. Kamilaris, “Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learn- ing,” in Computer Analysis of Images and Patterns , N. Tsapatsoulis, A. Panayides, T. Theocharides, A. Lanitis, C. Pattichis, and M. Vento, E...
[71]

Weakly su- pervised change detection using guided anisotropic diffusion,

R. C. Daudt, B. Le Saux, A. Boulch, and Y . Gousseau, “Weakly su- pervised change detection using guided anisotropic diffusion,” Machine Learning, vol. 112, no. 6, pp. 2211–2237, 2023

2023
[72]

A Weakly Supervised Convolutional Network for Change Segmentation and Classification,

P. Andermatt and R. Timofte, “A Weakly Supervised Convolutional Network for Change Segmentation and Classification,” in Computer Vision – ACCV 2020 Workshops , I. Sato and B. Han, Eds. Springer International Publishing, vol. 12628, pp. 103–119

2020
[73]

Revolution- izing building damage detection: A novel weakly supervised approach using high-resolution remote sensing images,

W. Qiao, L. Shen, Q. Wen, Q. Wen, S. Tang, and Z. Li, “Revolution- izing building damage detection: A novel weakly supervised approach using high-resolution remote sensing images,” International Journal of Digital Earth, vol. 17, no. 1, p. 2298245, 2024

2024
[74]

Semisuper- vised Change Detection Using Graph Convolutional Network,

S. Saha, L. Mou, X. X. Zhu, F. Bovolo, and L. Bruzzone, “Semisuper- vised Change Detection Using Graph Convolutional Network,” IEEE Geoscience and Remote Sensing Letters , vol. 18, no. 4, pp. 607–611
[75]

Dynamic graph- level neural network for sar image change detection,

R. Wang, L. Wang, X. Wei, J.-W. Chen, and L. Jiao, “Dynamic graph- level neural network for sar image change detection,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2021

2021
[76]

Hyperspectral change detection using semi- supervised graph neural network and convex deep learning,

T.-H. Lin and C.-H. Lin, “Hyperspectral change detection using semi- supervised graph neural network and convex deep learning,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[77]

SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention,

C. Sun, J. Wu, H. Chen, and C. Du, “SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention,” vol. 14, no. 12, p. 2801
[78]

Semi-Supervised Change Detection Based on Graphs with Generative Adversarial Networks,

J. Liu, K. Chen, G. Xu, H. Li, M. Yan, W. Diao, and X. Sun, “Semi-Supervised Change Detection Based on Graphs with Generative Adversarial Networks,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, pp. 74–77

2019
[79]

Detection of changes in buildings in remote sensing images via self-supervised contrastive pre- training and historical geographic information system vector maps,

W. Feng, F. Guan, J. Tu, C. Sun, and W. Xu, “Detection of changes in buildings in remote sensing images via self-supervised contrastive pre- training and historical geographic information system vector maps,” Remote Sensing, vol. 15, no. 24, p. 5670, 2023

2023
[80]

A hyperspectral image change detection framework with self-supervised contrastive learning pretrained model,

X. Ou, L. Liu, S. Tan, G. Zhang, W. Li, and B. Tu, “A hyperspectral image change detection framework with self-supervised contrastive learning pretrained model,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7724–7740, 2022

2022
[81]

A transformer- based neural network with improved pyramid pooling module for change detection in ecological redline monitoring,

Y . Zou, T. Shen, Z. Chen, P. Chen, X. Yang, and L. Zan, “A transformer- based neural network with improved pyramid pooling module for change detection in ecological redline monitoring,” Remote Sensing , vol. 15, no. 3, p. 588, 2023

2023
[82]

Self- supervised global–local contrastive learning for fine-grained change detection in vhr images,

F. Jiang, M. Gong, H. Zheng, T. Liu, M. Zhang, and J. Liu, “Self- supervised global–local contrastive learning for fine-grained change detection in vhr images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–13, 2023

2023
[83]

Contrastive self- supervised two-domain residual attention network with random aug- mentation pool for hyperspectral change detection,

Y . Huang, L. Zhang, W. Qi, C. Huang, and R. Song, “Contrastive self- supervised two-domain residual attention network with random aug- mentation pool for hyperspectral change detection,” Remote Sensing , vol. 15, no. 15, p. 3739, 2023

2023
[84]

Self-supervised learning for high-resolution remote sensing images change detection with varia- tional information bottleneck,

C. Wang, S. Du, W. Sun, and D. Fan, “Self-supervised learning for high-resolution remote sensing images change detection with varia- tional information bottleneck,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2023

2023
[85]

Self-supervised change detection in multi- view remote sensing images,

Y . Chen and L. Bruzzone, “Self-supervised change detection in multi- view remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021

2021
[86]

A self-supervised approach to pixel-level change detection in bi-temporal rs images,

——, “A self-supervised approach to pixel-level change detection in bi-temporal rs images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022

2022
[87]

Forest disturbance detection via self-supervised and transfer learning with sentinel-1&2 images,

R. S. Kuzu, O. Antropov, M. Molinier, C. O. Dumitru, S. Saha, and X. X. Zhu, “Forest disturbance detection via self-supervised and transfer learning with sentinel-1&2 images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2024

2024
[88]

Multicue contrastive self-supervised learning for change detection in remote sensing,

M. Yang, L. Jiao, F. Liu, B. Hou, S. Yang, Y . Zhang, and J. Wang, “Multicue contrastive self-supervised learning for change detection in remote sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–14, 2023

2023
[90]

Self-supervised multisensor change detection,

S. Saha, P. Ebel, and X. X. Zhu, “Self-supervised multisensor change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–10, 2022

2022
[91]

Detecting land cover changes between satellite image time series by exploiting self-supervised representation learning capabilities,

A. D. Adebayo, C. Pelletier, S. Lang, and S. Valero, “Detecting land cover changes between satellite image time series by exploiting self-supervised representation learning capabilities,” in IGARSS 2023- 2023 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2...

2023
[92]

Multi-scale self-supervised sar image change detection based on wavelet transform,

H. Zong, E. Zhang, X. Li, and H. Zhang, “Multi-scale self-supervised sar image change detection based on wavelet transform,” IEEE Geo- science and Remote Sensing Letters , 2024

2024
[93]

Ringmo: A remote sensing foundation model GRSM 18 with masked image modeling,

X. Sun, P. Wang, W. Lu, Z. Zhu, X. Lu, Q. He, J. Li, X. Rong, Z. Yang, H. Changet al., “Ringmo: A remote sensing foundation model GRSM 18 with masked image modeling,” IEEE Transactions on Geoscience and Remote Sensing, 2022

2022
[94]

Hybrid transformer network for change detection under self-supervised pretraining,

Y . Cui, Y . Zhuang, S. Dong, X. Zhang, P. Gao, H. Chen, and L. Chen, “Hybrid transformer network for change detection under self-supervised pretraining,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2023, pp. 6652– 6655

2023
[95]

Cmid: A unified self- supervised learning framework for remote sensing image understand- ing,

D. Muhtar, X. Zhang, P. Xiao, Z. Li, and F. Gu, “Cmid: A unified self- supervised learning framework for remote sensing image understand- ing,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[96]

Self-supervised pre-training via multi-modality images with transformer for change detection,

Y . Zhang, Y . Zhao, Y . Dong, and B. Du, “Self-supervised pre-training via multi-modality images with transformer for change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[97]

Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images,

P. Zhang, M. Gong, L. Su, J. Liu, and Z. Li, “Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 116, pp. 24–41, 2016

2016
[98]

Stacked fisher autoencoder for sar change detection,

G. Liu, L. Li, L. Jiao, Y . Dong, and X. Li, “Stacked fisher autoencoder for sar change detection,” Pattern Recognition , vol. 96, p. 106971, 2019

2019
[99]

Unsupervised multimodal change detection based on structural relationship graph representation learning,

H. Chen, N. Yokoya, C. Wu, and B. Du, “Unsupervised multimodal change detection based on structural relationship graph representation learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–18, 2022

2022
[100]

Change detection in synthetic aperture radar images based on deep neural networks,

M. Gong, J. Zhao, J. Liu, Q. Miao, and L. Jiao, “Change detection in synthetic aperture radar images based on deep neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 1, pp. 125–138, 2016

2016
[101]

Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network,

W. Zhao, Z. Wang, M. Gong, and J. Liu, “Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 12, pp. 7066–7080, 2017

2017
[102]

Spectral mapping with adversarial learning for unsupervised hyperspectral change detection,

J. Lei, M. Li, W. Xie, Y . Li, and X. Jia, “Spectral mapping with adversarial learning for unsupervised hyperspectral change detection,” Neurocomputing, vol. 465, pp. 71–83, 2021

2021
[103]

Ddpm-cd: Denoising diffusion probabilistic models as feature extractors for change detec- tion,

W. G. C. Bandara, N. G. Nair, and V . M. Patel, “Ddpm-cd: Denoising diffusion probabilistic models as feature extractors for change detec- tion,” 2024

2024
[104]

Background-Mixed Augmentation for Weakly Supervised Change De- tection,

R. Huang, R. Wang, Q. Guo, J. Wei, Y . Zhang, W. Fan, and Y . Liu, “Background-Mixed Augmentation for Weakly Supervised Change De- tection,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 7, pp. 7919–7927
[105]

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery,

Z. Zheng, A. Ma, L. Zhang, and Y . Zhong, “Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE, pp. 15 173–15 182

2021
[106]

A building change detection framework with patch-pairing single-temporal super- vised learning and metric guided attention mechanism,

S. Gao, K. Sun, W. Li, D. Li, Y . Tan, J. Wei, and W. Li, “A building change detection framework with patch-pairing single-temporal super- vised learning and metric guided attention mechanism,” International Journal of Applied Earth Observation and Geoinformation , vol. 129, p...

2024
[107]

Exchange means change: An unsupervised single-temporal change detection framework based on intra-and inter-image patch exchange,

H. Chen, J. Song, C. Wu, B. Du, and N. Yokoya, “Exchange means change: An unsupervised single-temporal change detection framework based on intra-and inter-image patch exchange,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 206, pp. 87–105, 2023

2023
[108]

Unified building change detection pre-training method with masked semantic annotations,

Y . Quan, A. Yu, W. Guo, X. Lu, B. Jiang, S. Zheng, and P. He, “Unified building change detection pre-training method with masked semantic annotations,” International Journal of Applied Earth Observation and Geoinformation, vol. 120, p. 103346, 2023

2023
[109]

Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery,

M. Seo, H. Lee, Y . Jeon, and J. Seo, “Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 6363–6372

2023
[110]

Data Augmentation and Few-Shot Change Detection in Forest Remote Sensing,

S. Zhu, W. Jing, P. Kang, M. Emam, and C. Li, “Data Augmentation and Few-Shot Change Detection in Forest Remote Sensing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 5919–5934
[111]

Scalable Multi- Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process,

Z. Zheng, S. Tian, A. Ma, L. Zhang, and Y . Zhong, “Scalable Multi- Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process,” in 2023 IEEE/CVF International Confer- ence on Computer Vision (ICCV) . IEEE, pp. 21 761–21 770

2023
[112]

A new learning paradigm for foundation model-based remote-sensing change detection,

K. Li, X. Cao, and D. Meng, “A new learning paradigm for foundation model-based remote-sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–12, 2024

2024
[113]

Segment any change,

Z. Zheng, Y . Zhong, L. Zhang, and S. Ermon, “Segment any change,” arXiv preprint arXiv:2402.01188 , 2024

2024 arXiv
[114]

Unsupervised deep change vector analysis for multiple-change detection in vhr images,

S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised deep change vector analysis for multiple-change detection in vhr images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 6, pp. 3677–3693, 2019

2019
[115]

Patch-level unsupervised planetary change detection,

S. Saha and X. X. Zhu, “Patch-level unsupervised planetary change detection,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022

2022
[116]

Deep metric learning for unsupervised remote sensing change detection,

W. G. C. Bandara and V . M. Patel, “Deep metric learning for unsupervised remote sensing change detection,” arXiv preprint arXiv:2303.09536, 2023

2023 arXiv
[117]

Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection,

J. Liu, K. Chen, G. Xu, X. Sun, M. Yan, W. Diao, and H. Han, “Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection,” IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 1, pp. 127–131
[118]

S 3 net: Superpixel-guided self-supervised learning network for multitemporal image change de- tection,

T. Zhan, M. Gong, X. Jiang, and E. Zhang, “S 3 net: Superpixel-guided self-supervised learning network for multitemporal image change de- tection,” IEEE Geoscience and Remote Sensing Letters , 2023

2023
[119]

Self-supervised representation learning for remote sensing image change detection based on temporal prediction,

H. Dong, W. Ma, Y . Wu, J. Zhang, and L. Jiao, “Self-supervised representation learning for remote sensing image change detection based on temporal prediction,” Remote Sensing , vol. 12, no. 11, p. 1868, 2020

2020
[120]

FixMatch: Simplifying Semi- Supervised Learning with Consistency and Confidence,

K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “FixMatch: Simplifying Semi- Supervised Learning with Consistency and Confidence,” in Advances in Neural Information Processing Systems, vol. 33. Curran Associates, Inc....

2020
[121]

Joint Self-Training and Rebalanced Consistency Learning for Semi-Supervised Change Detection,

X. Zhang, X. Huang, and J. Li, “Joint Self-Training and Rebalanced Consistency Learning for Semi-Supervised Change Detection,” vol. 61, pp. 1–13
[122]

C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images,

C. Han, M. Hu, and H. Chen, “C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images,” vol. 62
[123]

Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data,

S. Hafner, Y . Ban, and A. Nascetti, “Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data,” vol. 15, no. 21, p. 5135
[124]

Unsupervised change detection analysis in satellite image time series using deep learning combined with graph-based approaches,

E. Kalinicheva, D. Ienco, J. Sublime, and M. Trocan, “Unsupervised change detection analysis in satellite image time series using deep learning combined with graph-based approaches,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp...

2020
[125]

Point label meets re- mote sensing change detection: A consistency-aligned regional growth network,

L. Fang, Y . Jiang, H. Yu, Y . Zhang, and J. Yue, “Point label meets re- mote sensing change detection: A consistency-aligned regional growth network,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023
[126]

Weakly Supervised Semantic Change Detection via Label Refinement Frame- work,

Z. Zheng, Y . Liu, S. Tian, J. Wang, A. Ma, and Y . Zhong, “Weakly Supervised Semantic Change Detection via Label Refinement Frame- work,” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, pp. 2066–2069

2021
[127]

Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks,

S. H. Khan, X. He, F. Porikli, and M. Bennamoun, “Forest Change Detection in Incomplete Satellite Images With Deep Neural Networks,” vol. 55, no. 9, pp. 5407–5423
[128]

Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images,

N. Lu, C. Chen, W. Shi, J. Zhang, and J. Ma, “Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images,” vol. 12, no. 23, p. 3907
[129]

Multimodal artificial intelligence foundation models: Unleashing the power of remote sensing big data in earth observation,

D. Hong, C. Li, B. Zhang, N. Yokoya, J. A. Benediktsson, and J. Chanussot, “Multimodal artificial intelligence foundation models: Unleashing the power of remote sensing big data in earth observation,” The Innovation Geoscience , vol. 2, no. 1, p. 100055, 2024

2024
[130]

Generative adversarial nets,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , vol. 27, 2014

2014
[131]

Denoising diffusion probabilistic mod- els,

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic mod- els,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 6840–6851

2020
[132]

Fast unsupervised deep fusion network for change detection of mul- titemporal sar images,

“Fast unsupervised deep fusion network for change detection of mul- titemporal sar images,” Neurocomputing, vol. 332, pp. 56–70, 2019

2019
[133]

Hyperspectral anomaly change detection based on autoencoder,

M. Hu, C. Wu, L. Zhang, and B. Du, “Hyperspectral anomaly change detection based on autoencoder,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 3750– 3762, 2021

2021
[134]

Anomaly feature learning for unsupervised change detection in heterogeneous images: A deep sparse residual model,

R. Touati, M. Mignotte, and M. Dahmane, “Anomaly feature learning for unsupervised change detection in heterogeneous images: A deep sparse residual model,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 588–600, 2020

2020
[135]

Deep learning and superpixel feature extraction based on contractive autoencoder for GRSM 19 change detection in sar images,

N. Lv, C. Chen, T. Qiu, and A. K. Sangaiah, “Deep learning and superpixel feature extraction based on contractive autoencoder for GRSM 19 change detection in sar images,” IEEE Transactions on Industrial Informatics, vol. 14, no. 12, pp. 5530–5538, 2018

2018
[136]

Unsupervised change detection by cross-resolution difference learning,

X. Zheng, X. Chen, X. Lu, and B. Sun, “Unsupervised change detection by cross-resolution difference learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–16, 2022

2022
[137]

A deep convolutional coupling network for change detection based on heterogeneous optical and radar images,

J. Liu, M. Gong, A. K. Qin, and P. Zhang, “A deep convolutional coupling network for change detection based on heterogeneous optical and radar images,” IEEE Trans. Neural Networks Learn. Syst. , vol. 29, no. 3, pp. 545–559, 2018

2018
[138]

Iterative feature mapping network for detecting multiple changes in multi-source remote sensing images,

T. Zhan, M. Gong, J. Liu, and P. Zhang, “Iterative feature mapping network for detecting multiple changes in multi-source remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 146, pp. 38–51, 2018

2018
[139]

Log-based transformation feature learning for change detection in heterogeneous images,

T. Zhan, M. Gong, X. Jiang, and S. Li, “Log-based transformation feature learning for change detection in heterogeneous images,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 9, pp. 1352–1356, 2018

2018
[140]

Unsupervised change detection using convolutional-autoencoder multiresolution fea- tures,

L. Bergamasco, S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised change detection using convolutional-autoencoder multiresolution fea- tures,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–19, 2022

2022
[141]

Commonality autoencoder: Learning common features for change detection from heterogeneous images,

Y . Wu, J. Li, Y . Yuan, A. K. Qin, Q.-G. Miao, and M.-G. Gong, “Commonality autoencoder: Learning common features for change detection from heterogeneous images,” IEEE Transactions on Neural Networks and Learning Systems , vol. 33, no. 9, pp. 4257–4270, 2022

2022
[142]

Unsupervised change detection in multitemporal vhr images based on deep kernel pca convolutional mapping network,

C. Wu, H. Chen, B. Du, and L. Zhang, “Unsupervised change detection in multitemporal vhr images based on deep kernel pca convolutional mapping network,” IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 12 084–12 098, 2022

2022
[143]

Change detection in synthetic aperture radar images based on a generalized gamma deep belief networks,

M. Jia and Z. Zhao, “Change detection in synthetic aperture radar images based on a generalized gamma deep belief networks,” Sensors, vol. 21, no. 24, 2021

2021
[144]

Feature-level change detection using deep representation and feature change analysis for multispectral imagery,

H. Zhang, M. Gong, P. Zhang, L. Su, and J. Shi, “Feature-level change detection using deep representation and feature change analysis for multispectral imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 13, no. 11, pp. 1666–1670, 2016

2016
[145]

Unsuper- vised deep transfer learning-based change detection for hr multispectral images,

S. Saha, Y . T. Solano-Correa, F. Bovolo, and L. Bruzzone, “Unsuper- vised deep transfer learning-based change detection for hr multispectral images,” IEEE Geoscience and Remote Sensing Letters , vol. 18, no. 5, pp. 856–860, 2021

2021
[146]

Unsupervised change detection in satellite images with generative adversarial network,

C. Ren, X. Wang, J. Gao, X. Zhou, and H. Chen, “Unsupervised change detection in satellite images with generative adversarial network,”IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 12, pp. 10 047–10 061, 2021

2021
[147]

Unsupervised change detection based on image reconstruction loss,

H. Noh, J. Ju, M. Seo, J. Park, and D.-G. Choi, “Unsupervised change detection based on image reconstruction loss,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1352–1361

2022
[148]

A conditional adversarial net- work for change detection in heterogeneous images,

X. Niu, M. Gong, T. Zhan, and Y . Yang, “A conditional adversarial net- work for change detection in heterogeneous images,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 1, pp. 45–49, 2019

2019
[149]

Deep image translation with an affinity-based change prior for unsupervised multimodal change detection,

L. T. Luppino, M. Kampffmeyer, F. M. Bianchi, G. Moser, S. B. Serpico, R. Jenssen, and S. N. Anfinsen, “Deep image translation with an affinity-based change prior for unsupervised multimodal change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1...

2022
[150]

Unsupervised change detection from heterogeneous data based on image translation,

Z.-G. Liu, Z.-W. Zhang, Q. Pan, and L.-B. Ning, “Unsupervised change detection from heterogeneous data based on image translation,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022

2022
[151]

Generative adversarial networks under cutmix transforma- tions for multimodal change detection,

A. Radoi, “Generative adversarial networks under cutmix transforma- tions for multimodal change detection,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022

2022
[152]

Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing im- ages,

L. T. Luppino, M. A. Hansen, M. Kampffmeyer, F. M. Bianchi, G. Moser, R. Jenssen, and S. N. Anfinsen, “Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing im- ages,” IEEE Transactions on Neural Networks and Learning Systems , vol. 35, no. 1...

2024
[153]

Cd-gan: A robust fusion-based generative adversarial network for unsupervised remote sensing change detection with het- erogeneous sensors,

J.-J. Wang, N. Dobigeon, M. Chabert, D.-C. Wang, T.-Z. Huang, and J. Huang, “Cd-gan: A robust fusion-based generative adversarial network for unsupervised remote sensing change detection with het- erogeneous sensors,” Information Fusion, vol. 107, p. 102313, 2024

2024
[154]

Gcd-ddpm: A generative change detection model based on difference-feature-guided ddpm,

Y . Wen, X. Ma, X. Zhang, and M.-O. Pun, “Gcd-ddpm: A generative change detection model based on difference-feature-guided ddpm,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16, 2024

2024
[155]

Pcanet: A simple deep learning baseline for image classification?

T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y . Ma, “Pcanet: A simple deep learning baseline for image classification?” IEEE Transactions on Image Processing , vol. 24, no. 12, pp. 5017–5032, 2015

2015
[156]

Feature learning and change feature classification based on deep learning for ternary change detection in sar images,

M. Gong, H. Yang, and P. Zhang, “Feature learning and change feature classification based on deep learning for ternary change detection in sar images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 129, pp. 212–225, 2017

2017
[157]

Transferred deep learning-based change detection in remote sensing images,

M. Yang, L. Jiao, F. Liu, B. Hou, and S. Yang, “Transferred deep learning-based change detection in remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 9, pp. 6960–6973, 2019

2019
[158]

Local restricted convolutional neural network for change detection in polarimetric sar images,

F. Liu, L. Jiao, X. Tang, S. Yang, W. Ma, and B. Hou, “Local restricted convolutional neural network for change detection in polarimetric sar images,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 818–833, 2019

2019
[159]

Generative adversarial networks for change detection in multispectral imagery,

M. Gong, X. Niu, P. Zhang, and Z. Li, “Generative adversarial networks for change detection in multispectral imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 12, pp. 2310–2314, 2017

2017
[160]

A generative discriminatory classified network for change detection in multispectral imagery,

M. Gong, Y . Yang, T. Zhan, X. Niu, and S. Li, “A generative discriminatory classified network for change detection in multispectral imagery,” IEEE Journal of Selected Topics in Applied Earth Observa- tions and Remote Sensing , vol. 12, no. 1, pp. 321–333, 2019

2019
[161]

Unsupervised multiple change detection in remote sensing images via generative representa- tion learning network,

J. Shi, Z. Zhang, C. Tan, X. Liu, and Y . Lei, “Unsupervised multiple change detection in remote sensing images via generative representa- tion learning network,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022

2022
[162]

Binary change guided hy- perspectral multiclass change detection,

M. Hu, C. Wu, B. Du, and L. Zhang, “Binary change guided hy- perspectral multiclass change detection,” IEEE Transactions on Image Processing, vol. 32, pp. 791–806, 2023

2023
[163]

Combining iterative slow feature analysis and deep feature learning for change detection in high- resolution remote sensing images,

J. Xv, B. Zhang, H. Guo, J. Lu, and Y . Lin, “Combining iterative slow feature analysis and deep feature learning for change detection in high- resolution remote sensing images,” Journal of Applied Remote Sensing, vol. 13, no. 2, p. 024506, 2019

2019
[164]

Transfer learning-based bilinear convolutional networks for unsupervised change detection,

T. Zhan, M. Gong, X. Jiang, and W. Zhao, “Transfer learning-based bilinear convolutional networks for unsupervised change detection,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022

2022
[165]

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami,

J. Sublime and E. Kalinicheva, “Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami,”Remote Sensing, vol. 11, no. 9, p. 1123
[167]

Learning transferable visual models from natural language supervision,

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763

2021
[168]

Segment anything,

A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, P. Dollar, and R. Girshick, “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2023, pp. 4015–4026

2023
[169]

Segment anything is not always perfect: An investigation of sam on different real-world applications,

W. Ji, J. Li, Q. Bi, T. Liu, W. Li, and L. Cheng, “Segment anything is not always perfect: An investigation of sam on different real-world applications,” 2024

2024
[170]

Towards geospatial foundation models via continual pretraining,

M. Mendieta, B. Han, X. Shi, Y . Zhu, and C. Chen, “Towards geospatial foundation models via continual pretraining,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 16 806–16 816

2023
[171]

Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,

X. Guo, J. Lao, B. Dang, Y . Zhang, L. Yu, L. Ru, L. Zhong, Z. Huang, K. Wu, D. Hu et al. , “Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,” in Proceedings of the IEEE/CVF Conference on Computer Vision an...

2024
[172]

Cs-wscdnet: Class activation mapping and segment anything model-based framework for weakly supervised change detection,

L. Wang, M. Zhang, and W. Shi, “Cs-wscdnet: Class activation mapping and segment anything model-based framework for weakly supervised change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023

2023
[173]

Change detection between optical remote sensing imagery and map data via segment anything model (sam),

H. Chen, J. Song, and N. Yokoya, “Change detection between optical remote sensing imagery and map data via segment anything model (sam),” arXiv preprint arXiv:2401.09019 , 2024

2024 arXiv
[174]

Changeclip: Remote sens- ing change detection with multimodal vision-language representation learning,

S. Dong, L. Wang, B. Du, and X. Meng, “Changeclip: Remote sens- ing change detection with multimodal vision-language representation learning,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 208, pp. 53–69, 2024. GRSM 20

2024
[175]

Self-Supervised Pretraining via Multimodality Images With Transformer for Change Detection,

Y . Zhang, Y . Zhao, Y . Dong, and B. Du, “Self-Supervised Pretraining via Multimodality Images With Transformer for Change Detection,” vol. 61, pp. 1–11
[176]

Change event dataset for discovery from spatio-temporal remote sensing imagery,

U. Mall, B. Hariharan, and K. Bala, “Change event dataset for discovery from spatio-temporal remote sensing imagery,” in Advances in Neural Information Processing Systems , vol. 35, 2022, pp. 27 484– 27 496

2022
[177]

A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,

H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sensing, vol. 12, no. 10, 2020

2020
[178]

Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,

S. Ji, S. Wei, and M. Lu, “Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,” IEEE Transactions on geoscience and remote sensing , vol. 57, no. 1, pp. 574–586, 2018

2018
[179]

Urban change detection for multispectral earth observation using convolutional neural networks,

R. C. Daudt, B. Le Saux, A. Boulch, and Y . Gousseau, “Urban change detection for multispectral earth observation using convolutional neural networks,” in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . Ieee, 2018, pp. 2115–2118

2018
[180]

A change detection reality check,

I. Corley, C. Robinson, and A. Ortiz, “A change detection reality check,” arXiv preprint arXiv:2402.06994 , 2024

2024 arXiv
[181]

Change detection from very-high-spatial-resolution op- tical remote sensing images: Methods, applications, and future direc- tions,

D. Wen, X. Huang, F. Bovolo, J. Li, X. Ke, A. Zhang, and J. A. Benediktsson, “Change detection from very-high-spatial-resolution op- tical remote sensing images: Methods, applications, and future direc- tions,” IEEE Geoscience and Remote Sensing Magazine , vol. 9, no. 4, pp. 6...

2021
[182]

Land Cover Change Detection Techniques: Very-high-resolution optical images: A review,

Z. Lv, T. Liu, J. A. Benediktsson, and N. Falco, “Land Cover Change Detection Techniques: Very-high-resolution optical images: A review,” IEEE Geoscience and Remote Sensing Magazine , vol. 10, no. 1, pp. 44–63
[183]

Feature mutual representation based graph domain adaptive network for unsupervised hyperspectral change detection,

J. Qu, J. Zhao, W. Dong, S. Xiao, Y . Li, and Q. Du, “Feature mutual representation based graph domain adaptive network for unsupervised hyperspectral change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023

2023
[184]

Matching networks for one shot learning,

O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in neural information processing systems, vol. 29, 2016

2016
[185]

Learning what not to segment: A new perspective on few-shot segmentation,

C. Lang, G. Cheng, B. Tu, and J. Han, “Learning what not to segment: A new perspective on few-shot segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 8057–8067

2022
[186]

Polarity loss: Improving visual- semantic alignment for zero-shot detection,

S. Rahman, S. Khan, and N. Barnes, “Polarity loss: Improving visual- semantic alignment for zero-shot detection,” IEEE Transactions on Neural Networks and Learning Systems , 2022

2022
[187]

Plop: Learning with- out forgetting for continual semantic segmentation,

A. Douillard, Y . Chen, A. Dapogny, and M. Cord, “Plop: Learning with- out forgetting for continual semantic segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 4040–4050. Lei Ding (dinglei14@outlook.com) received his...

2021

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [26]

Multitask learning for large-scale semantic change detection,

R. C. Daudt, B. Le Saux, A. Boulch, and Y . Gousseau, “Multitask learning for large-scale semantic change detection,” Computer Vision and Image Understanding , vol. 187, p. 102783, 2019

2019

[2] [53]

Ecps: Cross pseudo supervision based on ensemble learning for semi-supervised remote sensing change detection,

Y . Yang, X. Tang, J. Ma, X. Zhang, S. Pei, and L. Jiao, “Ecps: Cross pseudo supervision based on ensemble learning for semi-supervised remote sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2024

2024

[3] [3]

Changer: Feature interaction is what you need for change detection,

S. Fang, K. Li, and Z. Li, “Changer: Feature interaction is what you need for change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–11, 2023

2023

[4] [48]

Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images,

Y . Zuo, L. Li, X. Liu, Z. Gao, L. Jiao, F. Liu, and S. Yang, “Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images,” vol. 62, pp. 1–15

[5] [166]

Domain Knowledge-Guided Self- Supervised Change Detection for Remote Sensing Images,

L. Yan, J. Yang, and J. Wang, “Domain Knowledge-Guided Self- Supervised Change Detection for Remote Sensing Images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 4167–4179

work page

[6] [89]

TD-SSCD: A Novel Network by Fusing Temporal and Differential Information for Self-Supervised Remote Sensing Image Change Detection,

Y . Qu, J. Li, X. Huang, and D. Wen, “TD-SSCD: A Novel Network by Fusing Temporal and Differential Information for Self-Supervised Remote Sensing Image Change Detection,” vol. 61, pp. 1–15

[7] [1]

Spectralgpt: Spectral remote sensing foundation model,

D. Hong, B. Zhang, X. Li, Y . Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia, A. Plaza, P. Gamba, J. A. Benediktsson, and J. Chanussot, “Spectralgpt: Spectral remote sensing foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 8, pp. 5227–5244, 2024

2024

[8] [2]

Remote sensing image change detection with transformers,

H. Chen, Z. Qi, and Z. Shi, “Remote sensing image change detection with transformers,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2021

2021