Pith. sign in

REVIEW 4 major objections 7 minor 66 references

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A data-augmentation and text-fusion pipeline improves long-tail UHR satellite segmentation by up to 3.33 mIoU, reaching state-of-the-art on three benchmarks.

desk verdict A plausible engineering combination with honest reporting, but single-run numbers and an inconsistent GID baseline mean the SOTA claim still needs multi-seed verification. read the letter →

arxiv 2504.19839 v1 pith:OYJKXLB3 submitted 2025-04-28 cs.CV

Yulong Guo , Zilun Zhang , Yongheng Shang , Tiancheng Zhao , Shuiguang Deng , Yingchun Yang , Jianwei Yin This is my paper · ORCID
classification cs.CV
keywords long-tailsemanticsegmentationultra-high-resolutionsatelliteimagerydataaugmentationmultimodalfusionSAM-HQresamplingGeoRSCLIPtextfeaturesclassimbalanceremotesensing
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 the long-tail class imbalance that limits ultra-high-resolution satellite image segmentation can be reduced by changing what the model sees during training rather than by redesigning the network. It introduces three training-side interventions: a multi-scale anchored cropping scheme, a SAM-HQ-based region resampling that favors tail classes while preserving ground sampling distance, and injection of remote-sensing text features from GeoRSCLIP into pixel features. On the URUR, GID, and FBP benchmarks, the full pipeline raises mean IoU by 3.33, 0.66, and 0.98 percentage points respectively over its Segformer baseline, reaching state-of-the-art numbers. A sympathetic reader would care because the results suggest that data composition and external text knowledge are underused levers for satellite-image segmentation.

What carries the argument

The framework's load-bearing objects are three training-time components. MSAR (Multi-Scale Anchored Region Sampling) crops a random anchor region and three larger regions at scale factors 2, 3, and 4 that each contain the anchor, downscales them to anchor size, and concatenates the four crops into one training image, giving the model more ground-area context without a multi-branch decoder. SRR-TA (Semantic Reranking and Resampling for Training Augmentation) runs SAM-HQ with a grid of prompt points to pre-extract object masks and bounding boxes, labels each box by its dominant class, and resamples boxes so that tail classes and category-rich regions appear more often in each batch while the original ground sampling distance is preserved whenever possible. The Injection of General Representation Knowledge maps 54 category-name text features from the frozen GeoRSCLIP text encoder through a single fully connected layer, computes the cosine similarity between each visual pixel feature and each text feature, concatenates the similarity map with the visual features, and passes the result to the segmentation decoder. The theoretical motor is the softmax cross-entropy gradient: within a batch, pixels not belonging to a class push that class's logit gradient in the opposite direction, so an approximately class-balanced batch regularizes updates in all class directions.

What would settle it

Run the text-feature injection on a new UHR dataset whose label set includes a class absent from the 54-term vocabulary, for example rangeland or village; the paper's coverage explanation predicts that class's IoU should drop relative to a no-text baseline, and if it does not drop, the proposed mechanism is wrong.

Watch

Extended reading notes

Core claim

The paper's central claim is that the long-tail problem in UHR satellite image segmentation is best attacked at the data and feature level. Concretely, it proposes that training a standard Segformer backbone with MSAR multi-scale anchored crops, SRR-TA resampling of SAM-HQ-detected object boxes with ground-sampling-distance preservation, and cosine-similarity fusion of GeoRSCLIP text features for 54 land-cover categories yields mIoU gains of 3.33% on URUR, 0.66% on GID, and 0.98% on FBP over its baseline, putting the method at state-of-the-art on those three datasets. The paper also claims that text injection only helps when the category vocabulary covers the labels in the target data, and shows that on WHU-OPT-SAR and DeepGlobe, where classes such as rangeland and village are missing from the 54-term list, the same injection hurts performance.

Load-bearing premise

The method's gains rest on the assumption that the hand-built 54-term category vocabulary covers every class the model must segment, and that the SAM-HQ-detected regions are complete geospatial objects whose features align with the CLIP-style training used by GeoRSCLIP.

Editorial extensions

If this is right

  • Data composition should be a first-class design axis for UHR segmentation, not a fixed preprocessing step.
  • SAM-HQ-detected object boxes, resampled with ground-sampling-distance preservation, can serve as a region-category sample bank for long-tail semantic segmentation.
  • Text features from a remote-sensing vision-language model can be fused pixel-wise without per-image captions, as long as the category vocabulary matches the label space.
  • On datasets with classes outside the vocabulary, the paper predicts and observes that text fusion degrades accuracy, so vocabulary coverage is a precondition for the method's use.
  • Balancing class directions within each training batch outperforms dataset-level tail oversampling, because global tail classes are not necessarily tail within a given batch.

Reading between the lines

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

  • A natural extension is to build the category vocabulary automatically from the training labels (or from a vision-language model's class space), which would remove the manual 54-term bottleneck that causes the WHU-OPT-SAR and DeepGlobe failures.
  • The gradient-balance argument implies that any sampling scheme that approximately balances per-class gradient directions in a batch should help; one could test whether a class-frequency-aware random cropper without SAM pre-segmentation captures part of the gain.
  • The per-class IoU changes could be probed against the cosine distances among class text embeddings: the method should help most for confusable classes whose text features are distinct, and least for classes whose text features overlap.
  • Since the text encoder is frozen, the module adds little training cost; an ablation varying the number of vocabulary terms would show how much of the gain comes from coverage versus from the fusion mechanism itself.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper proposes SRMF, a framework for long-tail semantic segmentation of ultra-high-resolution (UHR) satellite imagery. It introduces three components: MSAR, a multi-scale anchored region sampling scheme replacing random cropping; SRR-TA, a SAM-HQ-based region detection and resampling method that gives higher sampling priority to long-tail classes; and a multimodal fusion module that injects frozen GeoRSCLIP text features into visual features via cosine similarity and concatenation. The method is evaluated on five datasets (URUR, GID, FBP, WHU-OPT-SAR, DeepGlobe), reporting mIoU gains over a Segformer-MiT-B5 baseline of 3.33%, 0.66%, and 0.98% on URUR, GID, and FBP respectively, and claiming state-of-the-art performance. Ablations and comparisons are provided, and the paper honestly documents negative results on WHU-OPT-SAR and DeepGlobe when text vocabulary does not cover all target classes. Code is released.

Significance. If the reported gains are reliable, SRMF is a useful data-centric contribution to UHR remote sensing segmentation: it combines a sampling strategy that enlarges ground coverage with a training-time resampling scheme for tail classes, and it demonstrates that frozen, pretrained text features can be injected without per-image captions. The paper has genuine strengths: code is released, the ablation studies isolate the contribution of each component, the text encoder is frozen and was introduced in the authors' prior work, and the negative results on WHU-OPT-SAR and DeepGlobe are reported transparently rather than hidden. The central limitation is statistical: the headline improvements on GID and FBP are below one percentage point, no error bars or seed counts are given, and the baseline number for the main comparison is inconsistent across tables. The theoretical justification in Section III-C is also not a rigorous derivation. These issues affect the strength of the central performance claim even though the framework itself is plausible and the empirical design is otherwise reasonable.

major comments (4)
  1. [Table II vs Table VI, Section V-A and V-C] The GID baseline is internally inconsistent. Table II reports Segformer-MiT-B5 at 76.39 mIoU, implying an SRMF improvement of 1.08 points (77.47 - 76.39), while Table VI lists the same baseline as 76.81, implying the 0.66-point improvement quoted in the abstract and conclusion. Because the central claim is quantified by these deltas, the authors must reconcile the two baseline values, state which baseline is used for the headline numbers, and explain the discrepancy. This is load-bearing for the claimed improvement on GID.
  2. [All experimental tables, especially Tables II and VI] No variance information is provided for any reported mIoU. The absolute margins are small on GID (0.66 points), FBP (0.98 points), and URUR over the prior published WSDNet result (0.12 points in Table II). Without multiple seeds or a significance test, the reported improvements could fall within run-to-run variation of UHR segmentation training. The manuscript should report mean and standard deviation over at least three seeds, or otherwise justify why the single runs are representative. This directly affects the validity of the headline empirical claim.
  3. [Section III-C, Equations (1)-(3)] The theoretical motivation for SRR-TA is not a valid derivation. Equation (3) asserts that under a balanced batch the softmax output sums p_i and the label sums y_i approximately match, but softmax probabilities are determined by logits and by other classes, not by class frequencies alone; the claim that P_x (p_{x,i} - y_{x,i}) is approximately zero does not follow from class balance. The subsequent statement that this provides a regularizing effect is therefore unsupported. The manuscript should either provide a correct argument (e.g., relating to gradient norms of the cross-entropy term) or explicitly label Equations (1)-(3) as intuitive motivation rather than a proof. Since the paper states this is the theoretical foundation of SRR-TA, this is a load-bearing gap in the presentation, even if the empirical results are unaffected.
  4. [Section V-B and Table V] The multi-scale cropping scale factors and the top-K selection hyperparameters are important for the method, but the ablation for scale factors (Table V) is shown only for URUR and DeepGlobe, not for GID or FBP, and no sensitivity analysis is given for K (4 for GID/URUR, 8 for FBP) or for the WG-ResCro array length factor (0.07 in Algorithm 2). If the authors claim that MSAR and SRR-TA are robust components, they should provide at least a small sensitivity study on the datasets used in the headline results.
minor comments (7)
  1. [Section III-D, after Equation (6)] The text refers to 'the extracted feature Ff from Equation (3)', but Equation (3) is the class-balance condition in Section III-C; the intended reference appears to be Equation (6). Please correct the equation numbering.
  2. [Throughout] The name of the proposed cropping method is inconsistent: the introduction and contributions call it 'Multi-Scale Anchored Region Sampling (MARS)', while Section III-B, the ablation table, and the rest of the paper use 'MSAR'. Please standardize.
  3. [Section V-A, after Table II] The text says 'The performance of the SMRF method' in the discussion of Figure 5; this should read 'SRMF'.
  4. [Table IV] The column header 'Grennhouse' should be 'Greenhouse'. Also, the URUR per-class IoU for Background is extremely low (1.14, 0.01, 0.00, 1.06, 0.73, 1.34 across rows); a sentence explaining how background is defined and why it behaves this way would help readers interpret the mIoU values.
  5. [Section V-A] The sentence 'The experimental setup was in accordance with Section V' should refer to Section IV (Experimental Datasets and Setup), not Section V (Experimental Results).
  6. [References] References [33] and [48] appear to be the same arXiv preprint (Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation). Please merge or disambiguate them.
  7. [Section V-C, Table VI] The ablation table uses 'MARS' in the component column while the text uses 'MSAR' (see also the naming inconsistency above). In addition, the table would be easier to read if the baseline rows ('✗ ✗ ✗ ✗') were repeated with the same numerical values in both Table II and Table VI, which is not currently the case for GID.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the mIoU gains are empirical and the self-citations to GeoRSCLIP/RSSD are not load-bearing.

full rationale

SRMF's central claim is an empirical result on held-out test splits of URUR, GID, and FBP. The text features come from the frozen GeoRSCLIP encoder, which is not optimized on any target dataset; the learned components are the alignment layer, the 1x1 convolution logits, and the backbone/decoder, all trained on standard train splits. The reported mIoU improvements therefore measure generalization rather than reconstructing the training objective. The paper cites the authors' prior GeoRSCLIP and RSSD work, but the central claim does not rest on that citation alone: Table VI independently ablates the text-feature component, showing it helps on URUR/GID/FBP and hurts on WHU-OPT-SAR/DeepGlobe, which is an externally falsifiable result. The Section III-C gradient argument is only motivational; Eq. (3) is a stated condition that approximately balanced batches would satisfy, not a derived prediction of the method's output. The acknowledged vocabulary and SAM-HQ dependencies are real limitations, and the single-seed reporting and the GID baseline inconsistency are statistical correctness concerns, but none of these make a prediction equivalent to its input by construction.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central method introduces several hand-tuned hyperparameters (crop scales, top-K, category list size) and depends on assumptions about SAM-HQ and GeoRSCLIP behavior. No physical or architectural entities are postulated beyond standard learned components.

free parameters (4)
  • Multi-scale crop scale factors = [2, 3, 4] (with 1 for anchor)
    Section V-C and Table V: scale factors chosen after ablation; performance on URUR and DeepGlobe is highest with scales 1,2,3,4, indicating selection on validation data.
  • Top-K SAM-HQ regions per image = K=4 (URUR, GID), K=8 (FBP)
    Section V-C: set per dataset based on apparent validation tuning; no sensitivity analysis provided.
  • Text category list size = 54 categories
    Section III-D: K=54 categories are hand-integrated; coverage directly determines whether text fusion helps or hurts (negative results on WHU and DeepGlobe).
  • WG-ResCro array length factor = 0.07
    Algorithm 2: arr_len_factor = 0.07 is a hand-chosen constant controlling the exponential resampling weights.
assumptions (3)
  • domain assumption SAM-HQ produces segments that correspond to meaningful ground objects and can be labeled by ground-truth categories.
    The SRR-TA method relies on SAM-HQ masks to define region-level training samples with category labels. The paper notes that only 0 or very few regions are detected for some tail classes (e.g., snow), which limits the method.
  • domain assumption GeoRSCLIP text features provide useful class centers for the target categories.
    The Injection of General Representation Knowledge assumes that text features from a remote sensing VLM are beneficial. The paper shows this is false for categories omitted from the hand-built list, so the assumption is conditional.
  • ad hoc to paper Balanced class distribution within a training batch regularizes gradient updates.
    Section III-C, Eq. 3: the paper claims balanced batches yield sum(p - y) ≈ 0, but this holds for any batch due to softmax normalization, so the presented derivation does not establish the axiom.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation." pith.science (2026). https://pith.science/paper/OYJKXLB3

@misc{pith2026250419839,
  author       = {Pith},
  title        = {Pith review of: SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OYJKXLB3}},
  note         = {Machine review of arXiv:2504.19839}
}
read the original abstract

The long-tail problem presents a significant challenge to the advancement of semantic segmentation in ultra-high-resolution (UHR) satellite imagery. While previous efforts in UHR semantic segmentation have largely focused on multi-branch network architectures that emphasize multi-scale feature extraction and fusion, they have often overlooked the importance of addressing the long-tail issue. In contrast to prior UHR methods that focused on independent feature extraction, we emphasize data augmentation and multimodal feature fusion to alleviate the long-tail problem. In this paper, we introduce SRMF, a novel framework for semantic segmentation in UHR satellite imagery. Our approach addresses the long-tail class distribution by incorporating a multi-scale cropping technique alongside a data augmentation strategy based on semantic reordering and resampling. To further enhance model performance, we propose a multimodal fusion-based general representation knowledge injection method, which, for the first time, fuses text and visual features without the need for individual region text descriptions, extracting more robust features. Extensive experiments on the URUR, GID, and FBP datasets demonstrate that our method improves mIoU by 3.33\%, 0.66\%, and 0.98\%, respectively, achieving state-of-the-art performance. Code is available at: https://github.com/BinSpa/SRMF.git.

Figures

Figures reproduced from arXiv: 2504.19839 by the authors.

Figure 1
Figure 1. The distribution of class pixel counts in the training set of FBP dataset. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The architectural overview of our proposed approach is presented, wherein MARS denotes Multi-Scale Anchored Region Sampling, and SRR-TA [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Pre-extraction of multi-scale ground objects in three datasets. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The distribution of class pixel counts in the training set of URUR, [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: A comparative analysis of the SRMF method with other state-of-the-art (SOTA) approaches on the FBP dataset is presented. In (a), it can be observed [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: A comparative assessment of the SRMF method against other state-of-the-art (SOTA) methods on the URUR dataset is depicted. From (a), (b), and [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Comparison of Images Generated by RSSD and Sampled Images [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

66 extracted references · 43 canonical work pages

  1. [1]

    Road extraction methods in high-resolution remote sensing images: A comprehensive review,

    R. Lian, W. Wang, N. Mustafa, and L. Huang, “Road extraction methods in high-resolution remote sensing images: A comprehensive review,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5489–5507, 2020

    work page 2020

  2. [2]

    Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,

    Y . Li, B. Dang, W. Li, and Y . Zhang, “Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 20, 2024, pp. 22 213–22 221

    work page 2024

  3. [3]

    Brrnet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images,

    Z. Shao, P. Tang, Z. Wang, N. Saleem, S. Yam, and C. Sommai, “Brrnet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images,” Remote Sensing, vol. 12, no. 6, p. 1050, 2020

    work page 2020

  4. [4]

    A multimodal feature fusion network for building extraction with very high-resolution remote sensing image and lidar data,

    H. Luo, X. Feng, B. Du, and Y . Zhang, “A multimodal feature fusion network for building extraction with very high-resolution remote sensing image and lidar data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–19, 2024

    work page 2024

  5. [5]

    Applications in remote sensing to forest ecology and management,

    A. M. Lechner, G. M. Foody, and D. S. Boyd, “Applications in remote sensing to forest ecology and management,” One Earth , vol. 2, no. 5, pp. 405–412, 2020

    work page 2020

  6. [6]

    Remote sensing technology for mapping and monitoring land-cover and land-use change,

    J. Rogan and D. Chen, “Remote sensing technology for mapping and monitoring land-cover and land-use change,” Progress in planning , vol. 61, no. 4, pp. 301–325, 2004

    work page 2004

  7. [7]

    A comprehensive review of geospatial technology applications in earthquake preparedness, emergency management, and damage assessment,

    M. Shafapourtehrany, M. Batur, F. Shabani, B. Pradhan, B. Kalantar, and H. Özener, “A comprehensive review of geospatial technology applications in earthquake preparedness, emergency management, and damage assessment,” Remote Sensing, vol. 15, no. 7, p. 1939, 2023

    work page 1939

  8. [8]

    Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives,

    Y . Himeur, B. Rimal, A. Tiwary, and A. Amira, “Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives,” Information Fusion, vol. 86, pp. 44–75, 2022

    work page 2022

Show all 66 references
  1. [9]

    A review of remote sensing for environmental monitoring in china,

    J. Li, Y . Pei, S. Zhao, R. Xiao, X. Sang, and C. Zhang, “A review of remote sensing for environmental monitoring in china,” Remote Sensing, vol. 12, no. 7, p. 1130, 2020

    2020

  2. [10]

    Global open data remote sensing satellite missions for land monitoring and conservation: A review,

    D. Rado ˇcaj, J. Obho ¯daš, M. Juriši ´c, and M. Gašparovi ´c, “Global open data remote sensing satellite missions for land monitoring and conservation: A review,” Land, vol. 9, no. 11, p. 402, 2020

    2020

  3. [11]

    Identifying urban building function by integrating remote sensing imagery and poi data,

    A. Lin, X. Sun, H. Wu, W. Luo, D. Wang, D. Zhong, Z. Wang, L. Zhao, and J. Zhu, “Identifying urban building function by integrating remote sensing imagery and poi data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 8864– 8875, 2021

    2021

  4. [12]

    Detecting ecological spatial- temporal changes by remote sensing ecological index with local adapt- ability,

    D. Zhu, T. Chen, Z. Wang, and R. Niu, “Detecting ecological spatial- temporal changes by remote sensing ecological index with local adapt- ability,” Journal of Environmental Management , vol. 299, p. 113655, 2021

    2021

  5. [13]

    Iterdanet: Iterative intra-domain adaptation for semantic segmentation of remote sensing images,

    Y . Cai, Y . Yang, Y . Shang, Z. Chen, Z. Shen, and J. Yin, “Iterdanet: Iterative intra-domain adaptation for semantic segmentation of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2022

    2022

  6. [14]

    Bifdanet: Unsupervised bidirectional domain adaptation for semantic segmentation of remote sensing images,

    Y . Cai, Y . Yang, Q. Zheng, Z. Shen, Y . Shang, J. Yin, and Z. Shi, “Bifdanet: Unsupervised bidirectional domain adaptation for semantic segmentation of remote sensing images,” Remote Sensing, vol. 14, no. 1, p. 190, 2022

    2022

  7. [15]

    Db-blendmask: Decomposed attention and balanced blendmask for instance segmenta- tion of high-resolution remote sensing images,

    Z. Chen, Y . Shang, A. Python, Y . Cai, and J. Yin, “Db-blendmask: Decomposed attention and balanced blendmask for instance segmenta- tion of high-resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–15, 2022

    2022

  8. [16]

    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), 2018, pp. 801–818

    2018

  9. [17]

    Panoptic feature pyramid networks,

    A. Kirillov, R. Girshick, K. He, and P. Dollár, “Panoptic feature pyramid networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 6399–6408

    2019

  10. [18]

    Pyramid scene parsing network,

    H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890

    2017

  11. [19]

    U-net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 ...

    2015

  12. [20]

    Unet++: A nested u-net architecture for medical image segmenta- tion,

    Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmenta- tion,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8t...

    2018

  13. [21]

    Segformer: Simple and efficient design for semantic segmentation with transformers,

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems , vol. 34, pp. 12 077–12 090, 2021

    2021

  14. [22]

    Pvt v2: Improved baselines with pyramid vision transformer,

    W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pvt v2: Improved baselines with pyramid vision transformer,” Computational Visual Media , vol. 8, no. 3, pp. 415–424, 2022

    2022

  15. [23]

    Segmenter: Trans- former for semantic segmentation,

    R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Trans- former for semantic segmentation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 7262–7272

    2021

  16. [24]

    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

  17. [25]

    Swin transformer: Hierarchical vision transformer using shifted windows,

    Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022

    2021

  18. [26]

    Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,

    W. Chen, Z. Jiang, Z. Wang, K. Cui, and X. Qian, “Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 8924–8933

    2019

  19. [27]

    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

  20. [28]

    Icnet for real-time semantic segmentation on high-resolution images,

    H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia, “Icnet for real-time semantic segmentation on high-resolution images,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 405– 420

    2018

  21. [29]

    From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,

    Q. Li, W. Yang, W. Liu, Y . Yu, and S. He, “From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 7252–7261

    2021

  22. [30]

    Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,

    S. Guo, L. Liu, Z. Gan, Y . Wang, W. Zhang, C. Wang, G. Jiang, W. Zhang, R. Yi, L. Ma et al., “Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

    2022

  23. [31]

    Ehsnet: End-to-end holistic learning network for large-size remote sensing image semantic segmen- tation,

    W. Chen, Y . Li, B. Dang, and Y . Zhang, “Ehsnet: End-to-end holistic learning network for large-size remote sensing image semantic segmen- tation,” arXiv preprint arXiv:2211.11316 , 2022

    2022 arXiv

  24. [32]

    Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,

    D. Ji, F. Zhao, H. Lu, M. Tao, and J. Ye, “Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 23 621–23 630

    2023

  25. [34]

    Patch proposal network for fast semantic segmentation of high-resolution images,

    T. Wu, Z. Lei, B. Lin, C. Li, Y . Qu, and Y . Xie, “Patch proposal network for fast semantic segmentation of high-resolution images,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 402–12 409

    2020

  26. [35]

    Seeing beyond the patch: Scale- adaptive semantic segmentation of high-resolution remote sensing im- agery based on reinforcement learning,

    Y . Liu, S. Shi, J. Wang, and Y . Zhong, “Seeing beyond the patch: Scale- adaptive semantic segmentation of high-resolution remote sensing im- agery based on reinforcement learning,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 16 868–16 878

    2023

  27. [36]

    Enabling country-scale land cover mapping with meter-resolution satellite imagery,

    X.-Y . Tong, G.-S. Xia, and X. X. Zhu, “Enabling country-scale land cover mapping with meter-resolution satellite imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 196, pp. 178–196, 2023

    2023

  28. [37]

    Deep-learning-based semantic segmentation of remote sensing images: A survey,

    L. Huang, B. Jiang, S. Lv, Y . Liu, and Y . Fu, “Deep-learning-based semantic segmentation of remote sensing images: A survey,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 8370–8396, 2023

    2023

  29. [38]

    An improved algorithm for neural network classification of imbalanced training sets,

    R. Anand, K. G. Mehrotra, C. K. Mohan, and S. Ranka, “An improved algorithm for neural network classification of imbalanced training sets,” IEEE transactions on neural networks, vol. 4, no. 6, pp. 962–969, 1993

    1993

  30. [39]

    Under- standing imbalanced semantic segmentation through neural collapse,

    Z. Zhong, J. Cui, Y . Yang, X. Wu, X. Qi, X. Zhang, and J. Jia, “Under- standing imbalanced semantic segmentation through neural collapse,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 19 550–19 560. JOURNAL OF LATEX CLASS FIL...

    2023

  31. [40]

    Re- gion rebalance for long-tailed semantic segmentation,

    J. Cui, Y . Yuan, Z. Zhong, Z. Tian, H. Hu, S. Lin, and J. Jia, “Re- gion rebalance for long-tailed semantic segmentation,” arXiv preprint arXiv:2204.01969, 2022

    2022 arXiv

  32. [41]

    Borderline-smote: a new over- sampling method in imbalanced data sets learning,

    H. Han, W.-Y . Wang, and B.-H. Mao, “Borderline-smote: a new over- sampling method in imbalanced data sets learning,” in International conference on intelligent computing . Springer, 2005, pp. 878–887

    2005

  33. [42]

    C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,

    C. Drummond, R. C. Holte et al. , “C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,” in Workshop on learning from imbalanced datasets II , vol. 11, no. 1–8, 2003

    2003

  34. [43]

    Exploring the limits of weakly supervised pretraining,

    D. Mahajan, R. Girshick, V . Ramanathan, K. He, M. Paluri, Y . Li, A. Bharambe, and L. Van Der Maaten, “Exploring the limits of weakly supervised pretraining,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 181–196

    2018

  35. [44]

    Relay backpropagation for effective learning of deep convolutional neural networks,

    L. Shen, Z. Lin, and Q. Huang, “Relay backpropagation for effective learning of deep convolutional neural networks,” in Computer Vision– ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14 . Springer, 2016, pp. 467–482

    2016

  36. [45]

    Segment anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Loet al., “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 4015–4026

    2023

  37. [46]

    Segment anything in high quality,

    L. Ke, M. Ye, M. Danelljan, Y .-W. Tai, C.-K. Tang, F. Yuet al., “Segment anything in high quality,” Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  38. [47]

    Rethinking seman- tic segmentation: A prototype view,

    T. Zhou, W. Wang, E. Konukoglu, and L. Van Gool, “Rethinking seman- tic segmentation: A prototype view,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2582–2593

    2022

  39. [48]

    Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation,

    D. Ji, F. Zhao, and H. Lu, “Guided patch-grouping wavelet transformer with spatial congruence for ultra-high resolution segmentation,” arXiv preprint arXiv:2307.00711, 2023

    2023 arXiv

  40. [49]

    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

  41. [50]

    Rs5m: A large scale vision- language dataset for remote sensing vision-language foundation model,

    Z. Zhang, T. Zhao, Y . Guo, and J. Yin, “Rs5m: A large scale vision- language dataset for remote sensing vision-language foundation model,” arXiv preprint arXiv:2306.11300 , 2023

    2023 arXiv

  42. [51]

    Remoteclip: A vision language foundation model for remote sensing,

    F. Liu, D. Chen, Z. Guan, X. Zhou, J. Zhu, Q. Ye, L. Fu, and J. Zhou, “Remoteclip: A vision language foundation model for remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    2024

  43. [52]

    Rs-clip: Zero shot remote sensing scene classification via contrastive vision-language supervision,

    X. Li, C. Wen, Y . Hu, and N. Zhou, “Rs-clip: Zero shot remote sensing scene classification via contrastive vision-language supervision,” Inter- national Journal of Applied Earth Observation and Geoinformation, vol. 124, p. 103497, 2023

    2023

  44. [53]

    Semantic-sam: Segment and recognize anything at any granularity,

    F. Li, H. Zhang, P. Sun, X. Zou, S. Liu, J. Yang, C. Li, L. Zhang, and J. Gao, “Semantic-sam: Segment and recognize anything at any granularity,” arXiv preprint arXiv:2307.04767 , 2023

    2023 arXiv

  45. [54]

    Personalize segment anything model with one shot,

    R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, X. Ma, H. Dong, P. Gao, and H. Li, “Personalize segment anything model with one shot,” arXiv preprint arXiv:2305.03048, 2023

    2023 arXiv

  46. [55]

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

    L. Ding, K. Zhu, D. Peng, H. Tang, K. Yang, and L. Bruzzone, “Adapting segment anything model for change detection in vhr remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    2024

  47. [56]

    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

  48. [57]

    Evaluating the efficacy of segment anything model for delineating agriculture and urban green spaces in multiresolution aerial and spaceborne remote sensing images,

    B. Gui, A. Bhardwaj, and L. Sam, “Evaluating the efficacy of segment anything model for delineating agriculture and urban green spaces in multiresolution aerial and spaceborne remote sensing images,” Remote sensing, vol. 16, no. 2, p. 414, 2024

    2024

  49. [58]

    Mesam: Multiscale enhanced segment anything model for optical remote sensing images,

    X. Zhou, F. Liang, L. Chen, H. Liu, Q. Song, G. Vivone, and J. Chanus- sot, “Mesam: Multiscale enhanced segment anything model for optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, 2024

    2024

  50. [59]

    The segment anything model (sam) for remote sensing applications: From zero to one shot,

    L. P. Osco, Q. Wu, E. L. de Lemos, W. N. Gonçalves, A. P. M. Ramos, J. Li, and J. M. Junior, “The segment anything model (sam) for remote sensing applications: From zero to one shot,” International Journal of Applied Earth Observation and Geoinformation , vol. 124, p. 103540, 2023

    2023

  51. [60]

    Equalization loss for long-tailed object recognition,

    J. Tan, C. Wang, B. Li, Q. Li, W. Ouyang, C. Yin, and J. Yan, “Equalization loss for long-tailed object recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 662–11 671

    2020

  52. [61]

    Land-cover classification with high-resolution remote sensing images using transferable deep models,

    X.-Y . Tong, G.-S. Xia, Q. Lu, H. Shen, S. Li, S. You, and L. Zhang, “Land-cover classification with high-resolution remote sensing images using transferable deep models,” Remote Sensing of Environment , vol. 237, p. 111322, 2020

    2020

  53. [62]

    Mcanet: A joint semantic segmentation framework of optical and sar images for land use classification,

    X. Li, G. Zhang, H. Cui, S. Hou, S. Wang, X. Li, Y . Chen, Z. Li, and L. Zhang, “Mcanet: A joint semantic segmentation framework of optical and sar images for land use classification,” International Journal of Applied Earth Observation and Geoinformation , vol. 106, p. 102638, 2022

    2022

  54. [63]

    Deepglobe 2018: A challenge to parse the earth through satellite images,

    I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, and R. Raskar, “Deepglobe 2018: A challenge to parse the earth through satellite images,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 172–181

    2018

  55. [64]

    MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,

    M. Contributors, “MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,” https://github.com/open-mmlab/ mmsegmentation, 2020

    2020

  56. [65]

    Unified perceptual parsing for scene understanding,

    T. Xiao, Y . Liu, B. Zhou, Y . Jiang, and J. Sun, “Unified perceptual parsing for scene understanding,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 418–434

    2018

  57. [66]

    Fully convolutional networks for semantic segmentation,

    J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440

    2015

  58. [67]

    Boundary- enhanced dual-stream network for semantic segmentation of high- resolution remote sensing images,

    X. Li, L. Xie, C. Wang, J. Miao, H. Shen, and L. Zhang, “Boundary- enhanced dual-stream network for semantic segmentation of high- resolution remote sensing images,” GIScience & Remote Sensing , vol. 61, no. 1, p. 2356355, 2024

    2024

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.