3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection

Jincheng Zeng; Rui Huang; Sen Gao; Yan Xing

REVIEW 4 major objections 7 minor 63 references

3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Mamba-based change detector scans feature maps along height, width, and channel planes, reporting the best F1 on five remote sensing change-detection benchmarks.

desk verdict Useful but statistically fragile Mamba variant for change detection; the core 3D-scan benefit rests on 0.2-0.3 F1 differences that the paper's own tables do not consistently reproduce. read the letter →

arxiv 2506.19263 v1 pith:5XIHJFNR submitted 2025-06-24 cs.CV

Rui Huang , Jincheng Zeng , Sen Gao , Yan Xing This is my paper · ORCID

classification cs.CV

keywords remotesensingchangedetectionMambaselectivescan3D-SSMstatespacemodelspatiotemporalinteractionmulti-branchfeatureextractionbi-temporalimages

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

The paper claims that a Mamba-based change-detection model gains by running its selective scan along the channel axis as well as the two spatial axes. Mamba is a state-space sequence model whose selective scan processes a flattened sequence in linear time; earlier Mamba change detectors scanned only the height–width plane. The authors propose a 3D selective scan module (3D-SSM) that flattens a feature tensor along the HW, HC, and WC planes, scans each plane, and sums the three outputs. On top of it they build a spatiotemporal interaction module (SIM) for fusing bi-temporal features and a multi-branch decoder. Their experiments report the best F1 score among the compared methods on all five benchmark datasets, so the claim, if correct, is that channel-aware scanning is a practical gain for detecting changes in remote sensing imagery.

What carries the argument

The central object is the 3D selective scan module (3D-SSM), a variant of the Mamba selective scan that treats the channel axis as a scan direction alongside height and width. It carries the argument by replacing the HW-only SS2D scan: for each input tensor it forms sequences along the HW, HC, and WC planes, runs the bidirectional selective scan (the VME operation of Vision Mamba) on each, and sums the results, so that every output position receives context from both spatial neighbors and channel neighbors. Two further components hang off it: the spatiotemporal interaction module (SIM), which uses 3D-SSM to extract global features from both temporal images and combines them with local convolutional features through dynamic gating before taking an absolute difference; and the multi-branch feature extraction module (MBFEM), which fuses 3D-SSM with FFT and convolutional branches in the decoder. The module is the only part of the architecture that is novel at the scan level.

What would settle it

Train the model on WHU-CD and SYSU-CD with the HC and WC scan branches replaced by linear channel-mixing layers matched for parameter count and FLOPs, keeping the HW scan and all other modules unchanged; if the F1 score does not fall below the reported 95.30 and 84.90, the three-plane scanning geometry itself is not what carries the improvement.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that 3D-SSM captures global information from both the spatial plane and the channel perspective, giving a more comprehensive feature representation than the standard SS2D scan. For an input feature $F_{\mathrm{in}} \in \mathbb{R}^{H \times W \times C}$, the module flattens it into three sequences: $X \in \mathbb{R}^{(H \times W) \times C}$ over the HW plane, $X_H \in \mathbb{R}^{(W \times C) \times H}$, and $X_W \in \mathbb{R}^{(H \times C) \times W}$. Each sequence goes through the bidirectional VME scan, and the outputs combine as $F_{3D} = F_{\mathrm{out}} + F_{H}^{\mathrm{out}} + F_{W}^{\mathrm{out}}$. The authors build the SIM on this module to exchange global and local information between the two time points, and the MBFEM decoder combines convolution, FFT, and 3D-SSM branches. The reported F1 is the best among all compared methods on all five datasets; the ablations show the full three-plane combination is needed, since adding HC or WC to HW alone can lower F1.

Load-bearing premise

The load-bearing premise is that running the selective scan along the height–channel and width–channel planes creates useful cross-channel dependencies, rather than simply adding parameters and computation.

Editorial extensions

If this is right

Mamba-based change detectors can treat the channel axis as a first-class scan direction without changing the linear-time complexity of the selective scan.
The full three-plane scan is the operative recipe: on the two ablation datasets, HW+HC and HW+WC two-plane variants can reduce F1 below HW alone, while the three-plane combination improves it.
Pairing the 3D scan with FFT and convolution branches in the decoder is what the authors use to get both global context and boundary detail.
Reported F1 gains over the strongest Mamba baseline are 0.39% on WHU-CD, 0.31% on LEVIR-CD, 0.21% on CDD, 1.89% on SYSU-CD, and 3.74% on DSIFN-CD.

Reading between the lines

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

A testable prediction from the paper's Table IV is that channel-plane scanning is only useful when anchored by the spatial scan: the HC and WC branches may be aligning channel structure within a spatially organized context rather than providing independent information, and a per-stage ablation of which scan directions help at which resolution would clarify this.
If the mechanism is about spectral change, the method's margin over SS2D should grow on multi-spectral or hyper-spectral change-detection data, where change often appears as a spectral shift rather than a spatial edge.
Because the parameter count is modest but FLOPs are high, a matched-FLOP comparison against a wider SS2D model is the cleanest check of whether the three-plane scan adds representational power or just capacity.

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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

You should know this paper as a plausible but shaky architecture proposal. The authors replace the usual 2D selective scan in a Mamba backbone with a three-plane scan (HW, HC, WC) and add a spatiotemporal interaction module (SIM) and a multi-branch decoder (MBFEM). They report top F1 on five standard change-detection datasets and a code link. The architecture is clearly described and the ablation set is broad. Honest limitation section, too. That part is fine.

The real problem is the evidence for the central claim. The direct comparison of 3D-SSM versus SS2D is Table III: gains of 0.25 F1 on WHU-CD and 0.34 on SYSU-CD. Table IV repeats the HW-only configuration and reports the same WHU value but 84.79 instead of 84.56 on SYSU-CD. Same configuration, different numbers. That is a baseline drift almost as large as the claimed gain. Also, the Table IV narrative contradicts its own table: it says HW+HC or HW+WC reduce F1, but HW+WC improves F1 over HW-only on both datasets, and the HC+WC claim does not match the table either. No error bars, no repeated runs, no significance tests anywhere. For these benchmarks, 0.2-0.3 F1 differences are within run-to-run noise. The larger claims against M-CD (up to 3.74 F1 on DSIFN-CD) are more credible but still single runs.

Two smaller issues: the closest prior channel-axis scanning work, 3DSS-Mamba [51], is cited but never compared against, and the SIM equations have a typo (the second argument in Eq. 8 should likely be F1_L, not F1_G). Minor, but easy to fix.

Credit where due: the full model beats the strongest Mamba baseline on all five datasets, the backbone swap in Table VIII shows a +0.74 F1 improvement from replacing SS2D with 3D-SSM, and the design is explained well enough that someone could reimplement it. The inconsistency between Table III and Table IV is the load-bearing flaw: it makes the main causal assertion untestable as written. That does not mean the idea is wrong, just that this paper does not yet demonstrate it.

Who is this for? People working on Mamba-based change detection who want a concrete alternative scan strategy. It is not paradigm-shifting. I would send it to peer review because the architecture is plausible and the experimental scope is wide, but the authors need to redo the ablations with multiple seeds, fix the baseline drift, and add the missing 3DSS-Mamba comparison. My verdict: engage, but do not trust the headline numbers until those are addressed.

Referee Report

4 major / 7 minor

Summary. The manuscript proposes 3D-SSM, a selective scan module that, in addition to the standard HW spatial scan, also scans feature tensors along the HC and WC planes. The module is embedded in a change-detection framework together with a spatiotemporal interaction module (SIM) and a multi-branch feature extraction module (MBFEM), and is compared against 13 prior methods on five remote sensing change detection benchmarks. The central claim is that scanning along all three planes improves change detection over the HW-only SS2D scan.

Significance. If the effect is real, 3D-SSM is a simple and potentially useful extension of selective scanning for change detection. The paper's strengths are its clear architecture description, the release of code, and a broad experimental campaign with five datasets and many ablations. However, the central claim rests on small, single-run F1 differences, and the ablation tables contain internal inconsistencies and an uncontrolled baseline. The significance of the paper therefore depends on whether these issues can be resolved with corrected and statistically grounded experiments.

major comments (4)

[§IV-C2, Table IV] The narrative and the table contradict each other. The text states that combining HW with either HC or WC 'resulted in reduced F1 scores,' but Table IV shows HW+WC improves F1 over HW-only on both datasets (95.22 vs. 95.05 on WHU-CD; 84.85 vs. 84.79 on SYSU-CD). The text also states that HC+WC improves F1 by 0.17% and 0.06% relative to SS2D, whereas the table shows 95.05 vs. 95.05 on WHU-CD and 84.70 vs. 84.79 on SYSU-CD. Because this table is the direct evidence for the three-plane scanning design, the internal inconsistency makes the stated ablation conclusions untestable.
[§IV-C1 and §IV-C2, Tables III and IV] The SS2D baseline is not stable across the two ablation tables. On SYSU-CD, Table III reports SS2D F1=84.56 and OA=92.96, while Table IV reports the HW-only configuration as F1=84.79 and OA=93.08. This 0.23-point F1 drift between supposedly identical configurations is close to the 0.34-point F1 improvement attributed to 3D-SSM in Table III. The comparison is therefore not controlled, and the reader cannot attribute the reported gains to 3D-SSM rather than to run-to-run variation or an undocumented protocol change.
[§IV-C, Tables III-V] All key comparisons are single runs with no error bars, multiple seeds, or significance tests. The decisive differences, such as 3D-SSM vs. SS2D (+0.25 F1 on WHU-CD and +0.34 F1 on SYSU-CD in Table III; +0.25 and +0.11 in Table IV), are small relative to the baseline drift visible between Tables III and IV. The paper should provide repeated-run statistics or an equivalent significance analysis before claiming that 3D-SSM is superior to SS2D.
[§IV-C6, Table VIII] The within-backbone comparison of 3D-SSM against SS2D is not capacity-matched. Replacing SS2D with 3D-SSM in the VSSB increases parameters from 10.63M to 73.34M and FLOPs from 281.17G to 503.21G at batch size 8, yet the reported gain is 0.74 F1 on WHU-CD. Such a large increase in model capacity can account for the improvement, so this experiment does not isolate the effect of scanning along the HC and WC planes.

minor comments (7)

[Eq. (2)] The abbreviation 'VEM' appears in the equation while the text defines 'VME'; please unify the terminology and define the acronym at first use.
[§III-C, Eqs. (8)-(9)] The text states that F1_G is used to enhance F2_G and F2_L, but the equations show F1_G being gated by F2 features; please align the text and the equations.
[§III-E, Table VII] The loss is called cross-entropy in Eq. (18) but 'BCE loss' in the Table VII discussion; please use one consistent term.
[§III-D, Eqs. (13)-(15)] The 'scaling' branch shown in Fig. 2(d) is not described in the equations; please clarify how the scaling branch contributes to the MBFEM output.
[Contributions] The phrase 'spatialtemporal interaction module' should be 'spatiotemporal interaction module'.
[§II-C] The relation to 3DSS-Mamba [51], which also scans spatial and spectral dimensions, should be stated more precisely so that the novelty of scanning the HC and WC planes of a 2D feature map is clear.
[§IV-A2, Table I] For DSIFN-CD, the test set contains only 192 image pairs; please state whether the reported metrics are computed on that small test set and comment on the resulting variance.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central 3D-SSM claim is an empirical architecture comparison, not a derivation that reduces to its inputs.

full rationale

The paper does not derive the benefit of 3D-SSM from a definitional identity. Equation (5) defines the module output as a sum of HW-, HC-, and WC-scan components, but that summation is not equivalent to the measured F1 scores; the superiority claim rests on direct empirical comparison with SS2D and channel-attention alternatives (Table III), perspective ablations (Table IV), and 13 external baselines (Table I). No parameter is fitted to a benchmark subset and then reported as a prediction of that same subset, and no uniqueness theorem is imported. The only identifiable author self-citation is Ref. [34] in the related-work discussion ('emphasizing detailed edge features through hierarchical convolutions [34]'), which is contextual and not load-bearing; the backbone VMamba is cited to external work [28]. The skeptic's concern that the SS2D baseline differs between Table III (F1=84.56, OA=92.96 on SYSU-CD) and Table IV (F1=84.79, OA=93.08) is a legitimate experimental reproducibility and controlled-comparison issue, but it is not circularity: the paper's equations do not force the reported gains, and the central claim remains externally testable against benchmarks. Thus no circular reduction is exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on an architectural design choice (three-plane scanning) and a small set of tuned hyperparameters. No new physical entities or fitted physical constants are introduced. The most fragile elements are the assumption that channel-axis flattening yields meaningful cross-channel modeling and the reliance on a pretrained backbone without domain-specific verification.

free parameters (5)

Loss weight λ1 = 0.5
Coefficient for cross-entropy loss in the total loss; chosen via ablation on WHU-CD and SYSU-CD (Table VII) and applied to all datasets without per-dataset tuning.
Loss weight λ2 = 0.5
Coefficient for Dice loss; selected together with λ1 in the same ablation and fixed at 0.5 for all experiments.
Initial learning rate = 1e-4
Set in Section III-F for the Adam optimizer; a common default but not justified by analysis.
Batch size = 12 (8 for backbone replacement)
Reported in Section III-F; reduced to 8 for the 3D-SSM-in-backbone experiment due to GPU memory, which can affect results.
Training epochs = 100
All methods trained for 100 epochs per Section IV-A; no early stopping or convergence analysis is provided.

assumptions (4)

domain assumption Selective scan mechanism (S6) models long-range dependencies with linear complexity
Taken from Mamba [24]; the paper relies on this property to justify scanning-based modules without re-deriving it.
domain assumption Flattening features along HW, HC, and WC preserves spatial and pixel correspondence needed for change detection
The 3D-SSM design assumes that reshape and scan operations do not corrupt the needed spatial alignment; no explicit positional encoding is added after reshape.
domain assumption Pretrained VMamba-S backbone improves change detection performance
Table VIII shows pretraining helps, but the paper uses pretrained weights without specifying the pretraining dataset or verifying domain transfer to remote sensing imagery.
standard math FFT and inverse FFT operations are numerically stable and applicable to feature maps
Used in Eq. (16) without discussion of numerical stability, edge effects, or the choice of patch size in patch unfolding.

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Cite this review

Pith. "Pith review of 3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection." pith.science (2026). https://pith.science/paper/5XIHJFNR

@misc{pith2026250619263,
  author       = {Pith},
  title        = {Pith review of: 3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5XIHJFNR}},
  note         = {Machine review of arXiv:2506.19263}
}

read the original abstract

Existing Mamba-based approaches in remote sensing change detection have enhanced scanning models, yet remain limited by their inability to capture long-range dependencies between image channels effectively, which restricts their feature representation capabilities. To address this limitation, we propose a 3D selective scan module (3D-SSM) that captures global information from both the spatial plane and channel perspectives, enabling a more comprehensive understanding of the data.Based on the 3D-SSM, we present two key components: a spatiotemporal interaction module (SIM) and a multi-branch feature extraction module (MBFEM). The SIM facilitates bi-temporal feature integration by enabling interactions between global and local features across images from different time points, thereby enhancing the detection of subtle changes. Meanwhile, the MBFEM combines features from the frequency domain, spatial domain, and 3D-SSM to provide a rich representation of contextual information within the image. Our proposed method demonstrates favourable performance compared to state-of-the-art change detection methods on five benchmark datasets through extensive experiments. Code is available at https://github.com/VerdantMist/3D-SSM

Figures

Figures reproduced from arXiv: 2506.19263 by the authors.

**Figure 2.** The main framework of the proposed method. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Illustration of the dynamic gating (DG) module in SIM. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Typical change detection results on five datasets. White represents true positives, black represents true negatives, green represents false positives and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Feature visualization of MBFEM using SS2D or 3D-SSM. The boundaries of 3D-SSM are more accurate than those of SS2D. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Visualization of the predicted results and the feature maps of replacing 3D-SSM with SS2D and channel attention of MBFEM. The feature maps are [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

63 extracted references · 49 canonical work pages

[51]

3DSS-Mamba: 3D- Spectral-Spatial Mamba for Hyperspectral Image Classification,

Y . He, B. Tu, B. Liu, J. Li, and A. Plaza, “3DSS-Mamba: 3D- Spectral-Spatial Mamba for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16, 2024

work page 2024
[1]

M. D. Negassa, D. T. Mallie, and D. O. Gemeda, “Forest cover change detection using geographic information systems and remote sensing techniques: a spatio-temporal study on komto protected forest priority area, east wollega zone, ethiopia,” Environmental Systems Research , vol. 9, pp. 1–14, 2020

work page 2020
[2]

Spatiotemporal change and drivers analysis of desertification in the arid region of northwest china based on geographic detector,

H. Ding and H. Xingming, “Spatiotemporal change and drivers analysis of desertification in the arid region of northwest china based on geographic detector,”Environmental Challenges, vol. 4, p. 100082, 2021

work page 2021
[3]

A bibliometric and visualized analysis of remote sensing methods for glacier mass balance research,

A. Yu, H. Shi, Y . Wang, J. Yang, C. Gao, and Y . Lu, “A bibliometric and visualized analysis of remote sensing methods for glacier mass balance research,” Remote Sensing, vol. 15, no. 5, p. 1425, 2023

work page 2023
[4]

A multi-task model for sentiment aided stance detection of climate change tweets,

A. Upadhyaya, M. Fisichella, and W. Nejdl, “A multi-task model for sentiment aided stance detection of climate change tweets,” in Proceedings of the international AAAI conference on web and social media, vol. 17, 2023, pp. 854–865

work page 2023
[5]

Fast flood extent monitoring with sar change detection using google earth engine,

E. Hamidi, B. G. Peter, D. F. Mu ˜noz, H. Moftakhari, and H. Moradkhani, “Fast flood extent monitoring with sar change detection using google earth engine,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–19, 2023

work page 2023
[6]

Landslide detection methods based on deep learning in remote sensing images,

L. Wu, R. Liu, G. Li, J. Gou, and Y . Lei, “Landslide detection methods based on deep learning in remote sensing images,” in 2022 29th International Conference on Geoinformatics . IEEE, 2022, pp. 1–4

work page 2022
[7]

Evaluating urban building damage of 2023 kahramanmaras, turkey earthquake sequence using sar change detection,

X. Wang, G. Feng, L. He, Q. An, Z. Xiong, H. Lu, W. Wang, N. Li, Y . Zhao, Y . Wang et al. , “Evaluating urban building damage of 2023 kahramanmaras, turkey earthquake sequence using sar change detection,” Sensors, vol. 23, no. 14, p. 6342, 2023

work page 2023

Show all 63 references

[8]

A framework for crop yield estimation and change detection using image fusion of microwave and optical satellite dataset,

R. Kaur, R. K. Tiwari, R. Maini, and S. Singh, “A framework for crop yield estimation and change detection using image fusion of microwave and optical satellite dataset,” Quaternary, vol. 6, no. 2, p. 28, 2023

2023
[9]

Aerial image change detection using dual regions of interest networks,

P. Han, C. Ma, Q. Li, P. Leng, S. Bu, and K. Li, “Aerial image change detection using dual regions of interest networks,” Neurocomputing, vol. 349, pp. 190–201, 2019

2019
[10]

Enhanced self-attention network for remote sensing building change detection,

S. Liang, Z. Hua, and J. Li, “Enhanced self-attention network for remote sensing building change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 16, pp. 4900– 4915, 2023

2023
[11]

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 , 2018, pp. 2115–2118

2018
[12]

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 con- ference on image processing (ICIP) . IEEE, 2018, pp. 4063–4067

2018
[13]

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 , N. Navab, J. Horneg- ger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International ...

2015
[14]

Dual-dense convolution network for change detection of high-resolution panchromatic imagery,

W. Wiratama, J. Lee, S.-E. Park, and D. Sim, “Dual-dense convolution network for change detection of high-resolution panchromatic imagery,” Applied Sciences, vol. 8, no. 10, p. 1785, 2018

2018
[15]

Progressive context-aware aggregation network combining multi-scale and multi-level dense reconstruction for building change detection,

C. Xu, Z. Ye, L. Mei, W. Yang, Y . Hou, S. Shen, W. Ouyang, and Z. Ye, “Progressive context-aware aggregation network combining multi-scale and multi-level dense reconstruction for building change detection,” Remote Sensing, vol. 15, no. 8, p. 1958, 2023

1958
[16]

A deep siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images,

M. Wang, K. Tan, X. Jia, X. Wang, and Y . Chen, “A deep siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images,” Remote Sens- ing, vol. 12, no. 2, 2020

2020
[17]

Self-attention and convolution fusion network for land cover change detection over a new data set in wenzhou, china,

Y . Zhu, G. Jin, T. Liu, H. Zheng, M. Zhang, S. liang, j. Liu, and L. li, “Self-attention and convolution fusion network for land cover change detection over a new data set in wenzhou, china,” Remote Sensing , vol. 14, 11 2022

2022
[18]

Stnet: Spatial and temporal feature fusion network for change detection in remote sensing images,

X. Ma, J. Yang, T. Hong, M. Ma, Z. Zhao, T. Feng, and W. Zhang, “Stnet: Spatial and temporal feature fusion network for change detection in remote sensing images,” in 2023 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2023, pp. 2195–2200

2023
[19]

Adjacent-level feature cross-fusion with 3-d cnn for remote sensing image change detection,

Y . Ye, M. Wang, L. Zhou, G. Lei, J. Fan, and Y . Qin, “Adjacent-level feature cross-fusion with 3-d cnn for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–14, 2023

2023
[20]

Gcformer: Global context-aware transformer for remote sensing image change detection,

W. Yu, L. Zhuo, and J. Li, “Gcformer: Global context-aware transformer for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–12, 2024

2024
[21]

Multi- granularity siamese transformer-based change detection in remote sens- ing imagery,

L. Song, M. Xia, Y . Xu, L. Weng, K. Hu, H. Lin, and M. Qian, “Multi- granularity siamese transformer-based change detection in remote sens- ing imagery,” Engineering Applications of Artificial Intelligence , vol. 136, p. 108960, 2024

2024
[22]

Isnet: Towards improving separability for remote sensing image change detection,

G. Cheng, G. Wang, and J. Han, “Isnet: Towards improving separability for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022

2022
[23]

Multiscale spatial-channel transformer architecture search for remote sensing image change detection,

M. Zhang, L. Liu, Z. Lei, K. Ma, J. Feng, Z. Liu, and L. Jiao, “Multiscale spatial-channel transformer architecture search for remote sensing image change detection,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1–5, 2024

2024
[24]

Mamba: Linear-time sequence modeling with selective state spaces,

A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,” arXiv preprint arXiv:2312.00752 , 2023

2023 arXiv
[25]

Combining recurrent, convolutional, and continuous-time models with linear state space layers,

A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. R ´e, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” Advances in neural information processing systems, vol. 34, pp. 572–585, 2021

2021
[26]

Efficiently modeling long sequences with structured state spaces,

A. Gu, K. Goel, and C. Re, “Efficiently modeling long sequences with structured state spaces,” in International Conference on Learning Representations, 2022

2022
[27]

Vision mamba: Efficient visual representation learning with bidirectional state space model,

L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,” in Forty-first International Conference on Machine Learn- ing, 2024

2024
[28]

Vmamba: Visual state space model,

Y . Liu, Y . Tian, Y . Zhao, H. Yu, L. Xie, Y . Wang, Q. Ye, J. Jiao, and Y . Liu, “Vmamba: Visual state space model,” in Advances in Neural Information Processing Systems, vol. 37. Curran Associates, Inc., 2024, pp. 103 031–103 063

2024
[29]

Changemamba: Re- mote sensing change detection with spatiotemporal state space model,

H. Chen, J. Song, C. Han, J. Xia, and N. Yokoya, “Changemamba: Re- mote sensing change detection with spatiotemporal state space model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1– 20, 2024

2024
[30]

A mamba-based siamese network for remote sensing change detection,

J. N. Paranjape, C. de Melo, and V . M. Patel, “A mamba-based siamese network for remote sensing change detection,” arXiv preprint arXiv:2407.06839, 2024

2024 arXiv
[31]

Cdmamba: Remote sensing image change detection with mamba,

H. Zhang, K. Chen, C. Liu, H. Chen, Z. Zou, and Z. Shi, “Cdmamba: Remote sensing image change detection with mamba,” arXiv preprint arXiv:2406.04207, 2024

2024 arXiv
[32]

Fully convolutional siamese networks based change detection for optical aerial images with focal contrastive loss,

Z. Wang, C. Peng, Y . Zhang, N. Wang, and L. Luo, “Fully convolutional siamese networks based change detection for optical aerial images with focal contrastive loss,” Neurocomputing, vol. 457, pp. 155–167, 2021

2021
[33]

Deep multiscale siamese network with parallel convolutional structure and self-attention for change detection,

Q. Guo, J. Zhang, S. Zhu, C. Zhong, and Y . Zhang, “Deep multiscale siamese network with parallel convolutional structure and self-attention for change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021

2021
[34]

Change detection with various combinations of fluid pyramid integration networks,

R. Huang, M. Zhou, Y . Xing, Y . Zou, and W. Fan, “Change detection with various combinations of fluid pyramid integration networks,” Neu- rocomputing, vol. 437, pp. 84–94, 2021

2021
[35]

Snunet-cd: A densely connected siamese network for change detection of vhr images,

S. Fang, K. Li, J. Shao, and Z. Li, “Snunet-cd: A densely connected siamese network for change detection of vhr images,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2021

2021
[36]

Wricnet: A weighted rich-scale inception coder network for remote sensing image change detection,

Y . Jiang, L. Hu, Y . Zhang, and X. Yang, “Wricnet: A weighted rich-scale inception coder network for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1– 13, 2022

2022
[37]

Multi-annual change detec- tion using a weakly supervised 3d cnn in hr sits,

K. Meshkini, F. Bovolo, and L. Bruzzone, “Multi-annual change detec- tion using a weakly supervised 3d cnn in hr sits,” IEEE Geoscience and Remote Sensing Letters , 2024

2024
[38]

Exchanging dual-encoder– decoder: A new strategy for change detection with semantic guidance and spatial localization,

S. Zhao, X. Zhang, P. Xiao, and G. He, “Exchanging dual-encoder– decoder: A new strategy for change detection with semantic guidance and spatial localization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–16, 2023

2023
[39]

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
[40]

Mapformer: Boosting change detection by using pre-change information,

M. Bernhard, N. Strauß, and M. Schubert, “Mapformer: Boosting change detection by using pre-change information,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 16 837–16 846

2023
[41]

A transformer-based siamese net- work for change detection,

W. G. C. Bandara and V . M. Patel, “A transformer-based siamese net- work for change detection,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022, pp. 207–210

2022
[42]

Swinsunet: Pure transformer network for remote sensing image change detection,

C. Zhang, L. Wang, S. Cheng, and Y . Li, “Swinsunet: Pure transformer network for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022

2022
[43]

Change detection on remote sensing images using dual-branch multilevel intertemporal network,

Y . Feng, J. Jiang, H. Xu, and J. Zheng, “Change detection on remote sensing images using dual-branch multilevel intertemporal network,” 11 IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023

2023
[44]

Vct: Visual change transformer for remote sensing image change detection,

B. Jiang, Z. Wang, X. Wang, Z. Zhang, L. Chen, X. Wang, and B. Luo, “Vct: Visual change transformer for remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023

2023
[45]

Convtransnet: A cnn–transformer network for change detection with multiscale global–local representa- tions,

W. Li, L. Xue, X. Wang, and G. Li, “Convtransnet: A cnn–transformer network for change detection with multiscale global–local representa- tions,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023

2023
[46]

Mambahsi: Spatial- spectral mamba for hyperspectral image classification,

Y . Li, Y . Luo, L. Zhang, Z. Wang, and B. Du, “Mambahsi: Spatial- spectral mamba for hyperspectral image classification,” IEEE Transac- tions on Geoscience and Remote Sensing , 2024

2024
[47]

Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation,

X. Ma, X. Zhang, and M.-O. Pun, “Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation,” IEEE Geoscience and Remote Sensing Letters , 2024

2024
[48]

Mambair: A simple baseline for image restoration with state-space model,

H. Guo, J. Li, T. Dai, Z. Ouyang, X. Ren, and S.-T. Xia, “Mambair: A simple baseline for image restoration with state-space model,” in European Conference on Computer Vision , 2024, pp. 222–241

2024
[49]

Vmambair: Visual state space model for image restoration,

Y . Shi, B. Xia, X. Jin, X. Wang, T. Zhao, X. Xia, X. Xiao, and W. Yang, “Vmambair: Visual state space model for image restoration,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 01 2025

2025
[50]

Rsmamba: Remote sensing image classification with state space model,

K. Chen, B. Chen, C. Liu, W. Li, Z. Zou, and Z. Shi, “Rsmamba: Remote sensing image classification with state space model,” IEEE Geoscience and Remote Sensing Letters , 2024

2024
[52]

Rs-mamba for large remote sensing image dense prediction,

S. Zhao, H. Chen, X. Zhang, P. Xiao, L. Bai, and W. Ouyang, “Rs-mamba for large remote sensing image dense prediction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–14, 2024

2024
[53]

Glu variants improve transformer,

N. Shazeer, “Glu variants improve transformer,” arXiv preprint arXiv:2002.05202, 2020

2002 arXiv
[54]

A method for stochastic optimization,

D. Kinga, J. B. Adam et al. , “A method for stochastic optimization,” in International conference on learning representations (ICLR) , vol. 5. San Diego, California;, 2015, p. 6

2015
[55]

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
[56]

Axial cross attention meets cnn: Bibranch fusion network for change detection,

L. Song, M. Xia, L. Weng, H. Lin, M. Qian, and B. Chen, “Axial cross attention meets cnn: Bibranch fusion network for change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 21–32, 2022

2022
[57]

Changevit: Un- leashing plain vision transformers for change detection,

D. Zhu, X. Huang, H. Huang, Z. Shao, and Q. Cheng, “Changevit: Un- leashing plain vision transformers for change detection,” arXiv preprint arXiv:2406.12847, 2024

2024 arXiv
[58]

Elgc-net: Efficient local–global context aggregation for remote sensing change detection,

M. Noman, M. Fiaz, H. Cholakkal, S. Khan, and F. S. Khan, “Elgc-net: Efficient local–global context aggregation for remote sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–11, 2024

2024
[59]

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
[60]

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
[61]

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
[62]

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
[63]

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,

C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu, “A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 166, pp. 183–200, 2020

2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [51]

3DSS-Mamba: 3D- Spectral-Spatial Mamba for Hyperspectral Image Classification,

Y . He, B. Tu, B. Liu, J. Li, and A. Plaza, “3DSS-Mamba: 3D- Spectral-Spatial Mamba for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–16, 2024

work page 2024

[2] [1]

M. D. Negassa, D. T. Mallie, and D. O. Gemeda, “Forest cover change detection using geographic information systems and remote sensing techniques: a spatio-temporal study on komto protected forest priority area, east wollega zone, ethiopia,” Environmental Systems Research , vol. 9, pp. 1–14, 2020

work page 2020

[3] [2]

Spatiotemporal change and drivers analysis of desertification in the arid region of northwest china based on geographic detector,

H. Ding and H. Xingming, “Spatiotemporal change and drivers analysis of desertification in the arid region of northwest china based on geographic detector,”Environmental Challenges, vol. 4, p. 100082, 2021

work page 2021

[4] [3]

A bibliometric and visualized analysis of remote sensing methods for glacier mass balance research,

A. Yu, H. Shi, Y . Wang, J. Yang, C. Gao, and Y . Lu, “A bibliometric and visualized analysis of remote sensing methods for glacier mass balance research,” Remote Sensing, vol. 15, no. 5, p. 1425, 2023

work page 2023

[5] [4]

A multi-task model for sentiment aided stance detection of climate change tweets,

A. Upadhyaya, M. Fisichella, and W. Nejdl, “A multi-task model for sentiment aided stance detection of climate change tweets,” in Proceedings of the international AAAI conference on web and social media, vol. 17, 2023, pp. 854–865

work page 2023

[6] [5]

Fast flood extent monitoring with sar change detection using google earth engine,

E. Hamidi, B. G. Peter, D. F. Mu ˜noz, H. Moftakhari, and H. Moradkhani, “Fast flood extent monitoring with sar change detection using google earth engine,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–19, 2023

work page 2023

[7] [6]

Landslide detection methods based on deep learning in remote sensing images,

L. Wu, R. Liu, G. Li, J. Gou, and Y . Lei, “Landslide detection methods based on deep learning in remote sensing images,” in 2022 29th International Conference on Geoinformatics . IEEE, 2022, pp. 1–4

work page 2022

[8] [7]

Evaluating urban building damage of 2023 kahramanmaras, turkey earthquake sequence using sar change detection,

X. Wang, G. Feng, L. He, Q. An, Z. Xiong, H. Lu, W. Wang, N. Li, Y . Zhao, Y . Wang et al. , “Evaluating urban building damage of 2023 kahramanmaras, turkey earthquake sequence using sar change detection,” Sensors, vol. 23, no. 14, p. 6342, 2023

work page 2023