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REVIEW 3 major objections 5 minor 27 references

GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Semi-supervised change detection improves with two-level perturbation and a hardness gate

desk verdict A plausible two-level consistency method with a genuinely new gating idea, but the paper's own Table II contradicts its 'best on all six datasets' claim (CDD at 40%: UniMatch 90.7 vs. Ours 90.2 IoU). read the letter →

arxiv 2411.18880 v2 pith:CO2LENJB submitted 2024-11-28 cs.CV

Yan Xing , Qi'ao Xu , Zongyu Guo , Rui Huang , Yuxiang Zhang This is my paper · ORCID
classification cs.CV
keywords semi-supervisedchangedetectionconsistencyregularizationremotesensingstrong-to-weakfeatureperturbationhardness-awaregatingunlabeleddata
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 paper argues that semi-supervised change detection in remote sensing improves when unlabeled data are exploited at two levels at once: image-level strong-to-weak consistency and feature-level perturbation consistency. It adds a gating mechanism that estimates each unlabeled sample's difficulty by comparing two decoder outputs and applies feature perturbations only to samples above the median difficulty. On six change-detection benchmarks and at 5%, 10%, 20%, and 40% labeled training data, the method reports the highest IoU and overall accuracy among the prior methods it is compared with. If this holds, label-efficient change detection can become substantially more accurate with the same unlabeled data, which matters for mapping applications where hand-labeled change masks are scarce.

What carries the argument

The central mechanism is the gate-guided two-level perturbation consistency loop. The image-level branch enforces strong-to-weak consistency: two strongly augmented views of an unlabeled image pair must produce change maps matching the pseudo-label generated from the weakly augmented view, with confidence threshold 0.95. The feature-level branch applies seven perturbations (feature noise, feature dropout, object masking, context masking, guided cutout, intermediate VAT, and random dropout) to the shallow difference features $D_1$, and an auxiliary decoder must match the same pseudo-label. The hardness gate sits between them: for each sample it computes $IoU(P^{uw'}, P^{uw})$ between two decoder outputs and, using the median as the threshold, decides whether feature perturbation is applied. The gate is what makes the two-level treatment differential rather than uniform, and the paper's ablations attribute the remaining gain to it.

What would settle it

Run the gated variant against two controls on all six datasets at 5% labels: one that perturbs every unlabeled sample's features and one that perturbs none, keeping all other settings identical. The gate's median IoU threshold must beat both controls on mean IoU across datasets, and per-sample gate decisions should track per-sample error when ground-truth labels are revealed; otherwise the difficulty signal is not the cause of the reported gains.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that a two-level consistency scheme combined with a sample-difficulty gate yields state-of-the-art semi-supervised change detection. The labeled set trains the network with cross-entropy; unlabeled pairs receive two strong augmentations whose outputs are aligned with a weak-augmentation pseudo-label (image level), and shallow difference features are perturbed in several ways with auxiliary decoders whose outputs are aligned with the same pseudo-label (feature level). The gate computes $IoU(P^{uw'}, P^{uw})$ between the main and auxiliary decoder change maps for each sample; samples at or above the median $IoU$ get feature perturbations, while the rest do not. The paper reports that this combination beats all listed prior methods on all six datasets at all four label ratios, with the largest margins on WHU-CD and GZ-CD (for example, 4.3 and 5.2 IoU points over UniMatch at 5% labels).

Load-bearing premise

The gating mechanism assumes that agreement between the main decoder and an auxiliary decoder tells you which unlabeled image pairs are genuinely hard to learn, and that perturbing only the hard-looking pairs improves training; the paper tests this assumption with a single sensitivity plot on one dataset.

Editorial extensions

If this is right

  • At 5% labeled data, the method reaches IoU scores (83.0 on WHU-CD) above what the strongest prior baseline reaches with more labels, so low-label regimes become more practical.
  • Combining image-level and feature-level consistency is worth more than either alone: on WHU-CD at 5% labels, feature-only gives 68.0 IoU, image-only 77.4, and the combination 81.7.
  • The gate adds a reliable gain over ungated two-level consistency (83.0 vs 81.7 IoU at 5% on WHU-CD), so selective perturbation is a component of the reported result.
  • Perturbing only shallow difference features is more effective than perturbing deep features or both shallow and deep features on WHU-CD.
  • The method's compute cost is moderate (65.87 GFLOPs, 57.3 M parameters, 51.64 FPS), so the accuracy gain is not bought with a large inference-time overhead relative to prior adversarial and consistency baselines.

Reading between the lines

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

  • An extension the paper does not test is whether the median is the right gate threshold for every dataset and label ratio; on change-sparse scenes, trivial agreement between decoders may inflate IoU, and a data-dependent quantile might work better.
  • The decoder-disagreement gate is a general idea for sample-adaptive consistency regularization and could be lifted to other dense prediction tasks, with disagreement between two output heads replacing the two decoders.
  • The paper fixes the gate threshold as the median but does not study how the optimal threshold shifts as training progresses; a scheduled or learned threshold is a natural extension.
  • Because the reported comparison fixes all baselines at their default settings, an unstated corollary is that the margin may shrink if a strong baseline is given the same two-level perturbations without the gate.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes GTPC-SSCD, a semi-supervised change detection method that combines image-level strong-to-weak consistency with feature-level perturbation consistency, and introduces a hardness-analysis gating mechanism that uses the median IoU between two decoder outputs to decide whether to apply feature perturbations to a given unlabeled sample. The method is evaluated on six remote sensing change detection datasets at four labeled-data ratios (5%, 10%, 20%, 40%) against seven existing SSCD methods, with additional ablations and a complexity comparison. The paper claims that the proposed method achieves the best IoU and OA on all six datasets at all four label partitions.

Significance. If the empirical claim were fully supported, the paper would make a useful contribution to semi-supervised change detection by showing that combining image- and feature-level consistency with a sample-difficulty gate improves utilization of unlabeled data. The strengths of the paper include a broad experimental coverage (six datasets, four label ratios, seven baselines), a clearly described loss formulation, and an ablation study that isolates the contributions of the image-level, feature-level, and gating components. However, the central claim of universal superiority is contradicted by the paper's own Table II, and the lack of repeated runs or error bars makes it difficult to assess the reliability of the reported rankings. The gating mechanism's core assumption is also only validated on a single dataset and label ratio. The significance of the contribution is therefore not yet established at the level claimed.

major comments (3)
  1. [Section III.B, Table II] The statement that 'Our method achieves the best performance across all four partitions' is directly contradicted by Table II. In the CDD block at 40% labeled data, UniMatch reports IoU 90.7 and OA 98.81, while Ours reports IoU 90.2 and OA 98.74. This is not a marginal difference and it invalidates the claim of universal superiority made in the abstract and in Section III.B. The claim should be corrected and the discussion should address cases where the proposed method is not the best.
  2. [Section III.A, Table II, Sup-only rows] No error bars or repeated-seed results are reported, and the Sup-only rows show strongly non-monotonic behavior: on BCD, Sup-only IoU is 74.1 at 20% labels but drops to 70.1 at 40%; on GZ-CD, Sup-only IoU is 48.9 at 5% but only 36.9 at 10%. Such inconsistencies suggest high variance across runs or uncontrolled experimental conditions. Without multiple runs or a stated variance measure, the exact rankings in Table II, including the reported gains over UniMatch, cannot be taken as reliable evidence of superiority.
  3. [Section II.A, Eqs. (6)-(7), and Fig. 4] The gating mechanism assumes that the IoU between the outputs Puw' and Puw is a valid measure of sample hardness and that the median of these IoU scores is a stable, reliable threshold for deciding whether to apply feature perturbations. This assumption is load-bearing for the claimed contribution, yet it is validated only by one sensitivity plot on WHU-CD at 5% labeled data (Fig. 4). There is no evidence that the median threshold is appropriate across the other five datasets or across label ratios, and no analysis of how the gate interacts with the feature perturbation types. Additional ablations or a per-dataset analysis of the gate's effect are needed.
minor comments (5)
  1. [Section II.A, Eq. (4)] The notation Du4 and Du'1,1:K is not defined before its first use; the text should clarify which feature level Du4 refers to and how the perturbed difference features Du' are generated.
  2. [Section II.A, Eq. (7)] The median Mid(iou) is not precisely defined: it is unclear whether the median is computed over a mini-batch, over the whole unlabeled set, or over a moving statistic. This should be stated explicitly.
  3. [Table III] Table III omits RC-CD and SemiPTCD, which are included in the main comparison in Table II. The complexity comparison should either include all baselines or state why some are omitted.
  4. [Fig. 4] The caption refers to 'different perturbation ratios', but the method uses a median threshold. It would be clearer to explain how the perturbation ratio is varied and how it maps to the gating threshold.
  5. [Section II.B, heading] The heading 'Super-parameters' appears to be a typo for 'Hyper-parameters'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the method is an empirical consistency-regularization pipeline whose losses and gate are defined rather than derived, and its central claim is tested against independent benchmarks.

full rationale

GTPC-SSCD does not derive a prediction from a fitted input. The supervised loss (Eq. 1), image-level consistency loss (Eq. 3), feature-level consistency loss (Eq. 5), and total loss (Eq. 8) are definitions of training objectives, not derivations of a target result. The hardness-analysis gating mechanism in Eqs. (6)-(7) uses the model's own decoder IoU to decide whether to perturb a sample; this is self-referential in the sense of self-training or pseudo-labeling, but it is an architectural/algorithmic choice whose value is judged by external IoU/OA on held-out test sets. Nothing in the gating mechanism is fitted to the reported results, and the median threshold is not a parameter tuned to match Table II; it is a fixed statistic of the current batch. The single self-citation [4] appears only in a general applications sentence in the introduction and is not load-bearing for the method, so it does not constitute circularity. The strongest claim of universal superiority is contradicted by the paper's own Table II on CDD at 40% labels, where UniMatch achieves IoU 90.7 and OA 98.81 versus Ours 90.2 and 98.74; this is a correctness or reporting issue, not a circularity issue. No uniqueness theorem is imported, no ansatz is smuggled through self-citation, and no known result is simply renamed. The derivation chain is therefore self-contained, and the appropriate circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. The free parameters are standard hyperparameters and design choices, but none are fitted to the target benchmarks in a way that would circularly ensure the reported results; the gating threshold is computed from model outputs, which is a mild self-referential design. The main assumptions are about pseudo-label reliability and the validity of the hardness metric.

free parameters (4)
  • Pseudo-label confidence threshold tau = 0.95
    Set by hand in Eq. (2); determines which weak outputs become pseudo-labels. No sensitivity analysis reported.
  • Loss weights lambda1, lambda2, lambda3 = 0.5, 0.25, 0.25
    Fixed in Eq. (8); no ablation on these weights.
  • Number of feature perturbations K = 7
    Taken from cross-consistency training [20]; the paper does not ablate which subset is used.
  • Gating threshold ratio = median (50th percentile)
    Mid(iou) in Eq. (7); Fig. 4 shows sensitivity to perturbation ratio on one dataset, but the median is chosen as default.
assumptions (4)
  • domain assumption Pseudo-labels from weak augmentations at confidence 0.95 are accurate enough to supervise strong branches and feature-perturbed decoders.
    Used in Eqs. (2),(3),(5); if pseudo-labels are noisy early in training, both consistency losses propagate errors. Standard in FixMatch-style methods.
  • ad hoc to paper The IoU between two decoder outputs Puw' and Puw measures sample hardness.
    Eq. (6) defines hardness as decoder disagreement; no external validation that disagreement corresponds to meaningful difficulty.
  • ad hoc to paper The median IoU is a stable and reliable threshold for separating hard and easy samples.
    Stated in Section II.A; only one dataset (WHU-CD) is used to test sensitivity (Fig. 4).
  • domain assumption Feature perturbation on shallow features D1 is more beneficial than on deep features D4.
    Ablation in Table IV (FP(d1) vs FP(d4)); the reason given is that deep perturbations are 'excessive disruptions', a heuristic.

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Cite this review

Pith. "Pith review of GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection." pith.science (2026). https://pith.science/paper/CO2LENJB

@misc{pith2026241118880,
  author       = {Pith},
  title        = {Pith review of: GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CO2LENJB}},
  note         = {Machine review of arXiv:2411.18880}
}
read the original abstract

Semi-supervised change detection (SSCD) utilizes partially labeled data and abundant unlabeled data to detect differences between multi-temporal remote sensing images. The mainstream SSCD methods based on consistency regularization have limitations. They perform perturbations mainly at a single level, restricting the utilization of unlabeled data and failing to fully tap its potential. In this paper, we introduce a novel Gate-guided Two-level Perturbation Consistency regularization-based SSCD method (GTPC-SSCD). It simultaneously maintains strong-to-weak consistency at the image level and perturbation consistency at the feature level, enhancing the utilization efficiency of unlabeled data. Moreover, we develop a hardness analysis-based gating mechanism to assess the training complexity of different samples and determine the necessity of performing feature perturbations for each sample. Through this differential treatment, the network can explore the potential of unlabeled data more efficiently. Extensive experiments conducted on six benchmark CD datasets demonstrate the superiority of our GTPC-SSCD over seven state-of-the-art methods.

Figures

Figures reproduced from arXiv: 2411.18880 by the authors.

Figure 1
Figure 1. Motivation analysis of SSCD with different perturbation variants [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework of the proposed GTPC-SSCD method. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Detection results of different methods on six CD datasets at [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of different perturbation ratios at [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Reference graph

Works this paper leans on

27 extracted references · 22 canonical work pages

  1. [1]

    Change detection methods for remote sensing in the last decade: A comprehensive review,

    G. Cheng, Y . Huang, X. Li, S. Lyu, Z. Xu, H. Zhao, Q. Zhao, and S. Xiang, “Change detection methods for remote sensing in the last decade: A comprehensive review,” Remote Sensing , vol. 16, no. 13, p. 2355, 2024

    work page 2024

  2. [2]

    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 , vol. 12, pp. 1–18, 2023

    work page 2023

  3. [3]

    Time-series satellite remote sensing reveals gradually increasing war damage in the gaza strip,

    S. Holail, T. Saleh, X. Xiao, J. Xiao, G.-S. Xia, Z. Shao, M. Wang, J. Gong, and D. Li, “Time-series satellite remote sensing reveals gradually increasing war damage in the gaza strip,” National Science Review, vol. 11, p. nwae304, 08 2024

    work page 2024

  4. [4]

    Cross branch feature fusion decoder for consistency regularization-based semi-supervised change detection,

    Y . Xing, Q. Xu, J. Zeng, R. Huang, S. Gao, W. Xu, Y . Zhang, and W. Fan, “Cross branch feature fusion decoder for consistency regularization-based semi-supervised change detection,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9341–9345, 2024

    work page 2024

  5. [5]

    Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,

    T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. P ´erez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 2517–2526, 2019

    work page 2019

  6. [6]

    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, 2020

    work page 2020

  7. [7]

    Change detection of high-resolution remote sensing image based on semi-supervised segmentation and adversarial learning,

    S. Yang, S. Hou, Y . Zhang, H. Wang, and X. Ma, “Change detection of high-resolution remote sensing image based on semi-supervised segmentation and adversarial learning,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium , pp. 1055– 1058, 2022

    work page 2022

  8. [8]

    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, 2022

    work page 2022

Show all 27 references
  1. [9]

    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,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 16, pp. 3879–3891, 2023

    2023

  2. [10]

    Deep collaborative 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 collaborative learning with class-rebalancing for semi-supervised change detection in sar images,” Knowledge-Based Systems, vol. 264, p. 110281, 2023

    2023

  3. [11]

    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,” Remote Sensing, vol. 14, no. 12, p. 2801, 2022

    2022

  4. [12]

    Semibuildingchange: A semi- supervised high-resolution remote sensing image building change detec- tion method with a pseudo bi-temporal data generator,

    C. Sun, H. Chen, C. Du, and N. Jing, “Semibuildingchange: A semi- supervised high-resolution remote sensing image building change detec- tion method with a pseudo bi-temporal data generator,” IEEE Transac- tions on Geoscience and Remote Sensing , vol. 61, pp. 1–19, 2023

    2023

  5. [13]

    Semi-supervised remote sensing image change detection using mean teacher model for constructing pseudo- labels,

    Z. Mao, X. Tong, and Z. Luo, “Semi-supervised remote sensing image change detection using mean teacher model for constructing pseudo- labels,” in ICASSP 2023-2023 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP) , pp. 1–5, 2023

    2023

  6. [14]

    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,” Advances in Neural Information Processing Systems , vol. 33, pp. 596–608, 2020

    2020

  7. [15]

    Mtcnet: Multitask consistency network with single temporal supervision for semi-supervised building change detection,

    Q. Shu, J. Pan, Z. Zhang, and M. Wang, “Mtcnet: Multitask consistency network with single temporal supervision for semi-supervised building change detection,” International Journal of Applied Earth Observation and Geoinformation , vol. 115, p. 103110, 2022

    2022

  8. [16]

    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,” IEEE Trans- actions on Geoscience and Remote Sensing , 2023

    2023

  9. [17]

    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 Recognition (CVPR), pp. 7236–7246, 2023

    2023

  10. [18]

    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

  11. [19]

    Con- match: Semi-supervised learning with confidence-guided consistency regularization,

    J. Kim, Y . Min, D. Kim, G. Lee, J. Seo, K. Ryoo, and S. Kim, “Con- match: Semi-supervised learning with confidence-guided consistency regularization,” in Proceedings of the European conference on computer vision (ECCV) , pp. 674–690, Springer, 2022

    2022

  12. [20]

    Semi-supervised semantic segmen- tation with cross-consistency training,

    Y . Ouali, C. Hudelot, and M. Tami, “Semi-supervised semantic segmen- tation with cross-consistency training,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 12674–12684, 2020

    2020

  13. [21]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2016

    2016

  14. [22]

    Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

    L.-C. Chen, Y . Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder- decoder with atrous separable convolution for semantic image segmen- tation,” in Proceedings of the European conference on computer vision (ECCV), pp. 801–818, 2018

    2018

  15. [23]

    Semi-supervised semantic segmentation with high-and low-level consistency,

    S. Mittal, M. Tatarchenko, and T. Brox, “Semi-supervised semantic segmentation with high-and low-level consistency,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 4, pp. 1369– 1379, 2019

    2019

  16. [24]

    Revisiting consistency regularization for semi-supervised change detection in remote sensing images,

    W. G. C. Bandara and V . M. Patel, “Revisiting consistency regularization for semi-supervised change detection in remote sensing images,” arXiv preprint arXiv:2204.08454, 2022

    2022 arXiv

  17. [25]

    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, p. 1662, 2020

    2020

  18. [26]

    Afde-net: Building change detection using attention-based feature differential enhancement for satellite imagery,

    S. Holail, T. Saleh, X. Xiao, and D. Li, “Afde-net: Building change detection using attention-based feature differential enhancement for satellite imagery,”IEEE Geoscience and Remote Sensing Letters , vol. 20, pp. 1–5, 2023

    2023

  19. [27]

    Change detection in remote sensing images using conditional adver- sarial networks,

    M. Lebedev, Y . V . Vizilter, O. Vygolov, V . A. Knyaz, and A. Y . Rubis, “Change detection in remote sensing images using conditional adver- sarial networks,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 42, pp. 565–...

    2018

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