CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS

432 citations as recorded by crossref.

1. PeftCD: Leveraging Vision Foundation Models With Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection S. Dong et al. https://doi.org/10.1109/JSTARS.2026.3679260
2. PSTNet: Progressive Sampling Transformer Network for Remote Sensing Image Change Detection X. Song et al. https://doi.org/10.1109/JSTARS.2022.3204191
3. Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review G. Cheng et al. https://doi.org/10.3390/rs16132355
4. Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery J. Jia et al. https://doi.org/10.1109/JSTARS.2024.3384545
5. HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images C. Han et al. https://doi.org/10.1109/JSTARS.2023.3264802
6. Cooperative Alignment and Boundary Refinement Network via Multisource Prior Synergy for Optical Remote Sensing Change Detection C. Hu et al. https://doi.org/10.1109/JSTARS.2026.3730956
7. Cortical Folding-Based Spatial–Temporal Modeling for Joint Land-Cover Segmentation and Multi-Class Change Analysis S. Kasetty & R. Krishnan https://doi.org/10.1109/ACCESS.2026.3716127
8. Change Detection in Remote-Sensing Images Using Pyramid Pooling Dynamic Sparse Attention Network With Difference Enhancement Z. Li et al. https://doi.org/10.1109/JSTARS.2024.3374050
9. DFMamba: Difference-enhanced and adaptive frequency-aware Mamba for change detection P. Yuan et al. https://doi.org/10.1016/j.neunet.2026.109518
10. DAHT-Net: Deformable Attention-Guided Hierarchical Transformer Network Based on Remote Sensing Image Change Detection G. Shi et al. https://doi.org/10.1109/ACCESS.2023.3307642
11. A dual pixel-level and subpatch-level network with cross-temporal super resolution for change detection across spatial resolutions D. Wen et al. https://doi.org/10.1016/j.jag.2026.105134
12. HCGNet: A Hybrid Change Detection Network Based on CNN and GNN C. Zhang et al. https://doi.org/10.1109/TGRS.2023.3349069
13. Benchmarking attention mechanisms and consistency regularization semi-supervised learning for post-flood building damage assessment J. Yu et al. https://doi.org/10.1016/j.ijdrr.2025.105664
14. EG-SFENet: edge-guided spatio-temporal feature fusion enhancement network for remote sensing change detection Z. Guo et al. https://doi.org/10.1016/j.eswa.2026.132168
15. DEMFC-Net: A Differential Enhancement-Based Multiscale Feature Collaboration Network for Remote Sensing Image Change Detection Z. Liu et al. https://doi.org/10.1109/JSTARS.2025.3605972
16. Changes detection of remote sensing images using two-stream two-stream deep neural network Y. Hasanzadeh et al. https://doi.org/10.61186/jgst.13.1.55
17. MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images J. Ma et al. https://doi.org/10.3390/s22030888
18. One Token for Detecting Various Changes D. Wang et al. https://doi.org/10.1109/TGRS.2025.3536473
19. Real-CD: Change Detection Under Real-World Complex Interference via Dynamic Distribution Correction L. Fang et al. https://doi.org/10.1109/TIP.2026.3684407
20. Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images Z. Li et al. https://doi.org/10.1016/j.jag.2025.104393
21. CLHF-Net: A Channel-Level Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection J. Ma et al. https://doi.org/10.3390/sym14061138
22. CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery Z. Zheng et al. https://doi.org/10.1016/j.isprsjprs.2021.03.005
23. Unsupervised Global Difference Modeling for Image Change Detection W. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3588154
24. SSCFNet: A Spatial-Spectral Cross Fusion Network for Remote Sensing Change Detection J. Wang et al. https://doi.org/10.1109/JSTARS.2023.3267137
25. DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection C. Wang et al. https://doi.org/10.1109/JSTARS.2025.3531658
26. High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net Q. Wang et al. https://doi.org/10.3390/rs15143517
27. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al. https://doi.org/10.1109/JSTARS.2021.3070368
28. Spectral Knowledge Transfer for Remote Sensing Change Detection H. Zheng et al. https://doi.org/10.1109/TGRS.2023.3346879
29. Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change A. Olteanu-Raimond et al. https://doi.org/10.3390/rs12071186
30. Meta‐learning shows great potential in plant disease recognition under few available samples X. Wu et al. https://doi.org/10.1111/tpj.16176
31. High–Low Frequency Reduction Model for Real-Time Change Detection and Coregistration in Location Discrepancy Sensing Images X. Xu et al. https://doi.org/10.1109/LGRS.2021.3129771
32. An Empirical Study of Remote Sensing Pretraining D. Wang et al. https://doi.org/10.1109/TGRS.2022.3176603
33. Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck C. Wang et al. https://doi.org/10.1109/JSTARS.2023.3288294
34. CDMamba: Incorporating Local Clues Into Mamba for Remote Sensing Image Binary Change Detection H. Zhang et al. https://doi.org/10.1109/TGRS.2025.3545012
35. High-resolution triplet network with dynamic multiscale feature for change detection on satellite images X. Hou et al. https://doi.org/10.1016/j.isprsjprs.2021.05.001
36. KAN-UG: Kolmogorov–Arnold Network-Based Difference Feature Enhancement Method With Uncertainty Guidance for Change Detection S. Lin et al. https://doi.org/10.1109/TGRS.2026.3681936
37. HyperCLIP-CD: Hyperbolic Multimodal Representation Enhancement for Remote Sensing Change Detection Q. Yang et al. https://doi.org/10.1109/TGRS.2026.3670127
38. CTIDRNet: Cross-Temporal Interaction With Difference Refinement Network for Remote Sensing Image Change Detection K. Du et al. https://doi.org/10.1109/LGRS.2024.3507760
39. Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries J. Lee et al. https://doi.org/10.3390/app13042485
40. A Triple-Stream Network With Cross-Stage Feature Fusion for High-Resolution Image Change Detection Y. Zhao et al. https://doi.org/10.1109/TGRS.2022.3233849
41. FRCD: Feature Refine Change Detection Network for Remote Sensing Images Z. Wang et al. https://doi.org/10.1109/LGRS.2023.3303200
42. Exploring Generalizable Pretraining for Real-World Change Detection via Geometric Estimation Y. Zhao et al. https://doi.org/10.1109/TGRS.2025.3586639
43. HiFE-GAN: Semantic Synthesis of GPR B-Scan Images by High-Frequency Encoding GAN B. Wang & S. Wu https://doi.org/10.1109/TGRS.2025.3540974
44. Adaptive Separation Complementary Fusion Network for High-Resolution Image Change Detection W. Fu et al. https://doi.org/10.1109/TGRS.2026.3652181
45. DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images J. Chen et al. https://doi.org/10.1109/JSTARS.2020.3037893
46. Change Detection on Remote Sensing Images Using Multidimensional Attention Network Y. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3594716
47. CBSASNet: A Siamese Network Based on Channel Bias Split Attention for Remote Sensing Change Detection N. He et al. https://doi.org/10.1109/TGRS.2024.3405025
48. Mining Joint Intraimage and Interimage Context for Remote Sensing Change Detection F. Zhou et al. https://doi.org/10.1109/TGRS.2023.3275819
49. ELGC-Net: Efficient Local–Global Context Aggregation for Remote Sensing Change Detection M. Noman et al. https://doi.org/10.1109/TGRS.2024.3362914
50. Generalization-Aware Remote Sensing Change Detection via Domain-Agnostic Learning Q. Zang et al. https://doi.org/10.1109/TMM.2025.3613179
51. Difference Guidance Learning With Feature Alignment for Change Detection Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3440001
52. Machine and deep learning for sentinel-based land use and land cover change detection: a systematic review and future outlook A. Nigar et al. https://doi.org/10.1007/s10661-026-15558-w
53. Unsupervised Change Detection in Satellite Images With Generative Adversarial Network C. Ren et al. https://doi.org/10.1109/TGRS.2020.3043766
54. A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery G. Murdaca et al. https://doi.org/10.3390/rs15245664
55. Geometric Variation Adaptive Network for Remote Sensing Image Change Detection S. Huo et al. https://doi.org/10.1109/TGRS.2024.3363431
56. SPMNet: A Siamese Pyramid Mamba Network for Very-High-Resolution Remote Sensing Change Detection J. Wang et al. https://doi.org/10.1109/TGRS.2025.3565801
57. Cross-Resolution Change Detection in Remote Sensing via Unequal Relationships From a Frequency Perspective L. Ning et al. https://doi.org/10.1109/TGRS.2025.3590023
58. Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review S. Saidi et al. https://doi.org/10.3390/rs16203852
59. Spatial–Spectral and Channel–Spectral Differences Integration Network for Remote Sensing Image Change Detection Z. Lu et al. https://doi.org/10.1109/JSTARS.2025.3528185
60. Remote Sensing Image Change Detection Transformer Network Based on Dual-Feature Mixed Attention X. Song et al. https://doi.org/10.1109/TGRS.2022.3209972
61. GeSANet: Geospatial-Awareness Network for VHR Remote Sensing Image Change Detection X. Zhao et al. https://doi.org/10.1109/TGRS.2023.3272550
62. High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion C. Wang et al. https://doi.org/10.1155/2020/8360361
63. IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection Q. Guo et al. https://doi.org/10.1109/TETCI.2025.3529893
64. MSSI-Net: Multiscale Semantic-Guided Synergistic Interaction Network for Remote Sensing Image Change Detection S. Tian et al. https://doi.org/10.1109/TGRS.2025.3635202
65. MLDFNet: A Multilabel Dual-Flow Network for Change Detection in Bitemporal Remote Sensing Images D. Sidekejiang et al. https://doi.org/10.1109/JSTARS.2025.3530146
66. A data fusion-based framework to integrate multi-source VGI in an authoritative land use database L. Liu et al. https://doi.org/10.1080/17538947.2020.1842524
67. EFP-Net: A Novel Building Change Detection Method Based on Efficient Feature Fusion and Foreground Perception R. He et al. https://doi.org/10.3390/rs15225268
68. Integrating Local and Global Features via CNN and Mamba for Semi-Supervised Change Detection Z. Zhang et al. https://doi.org/10.1109/TIM.2025.3573775
69. A Novel Multibranch Self-Distillation Framework for Optimizing Remote Sensing Change Detection Z. Liu et al. https://doi.org/10.1109/JSTARS.2025.3597817
70. GeoFormer: A Geometric Representation Transformer for Change Detection J. Zhao et al. https://doi.org/10.1109/TGRS.2023.3331751
71. Knowledge Distillation-Based Lightweight Change Detection in High-Resolution Remote Sensing Imagery for On-Board Processing G. Wang et al. https://doi.org/10.1109/JSTARS.2024.3354944
72. SCA-CDNet: a robust siamese correlation-and-attention-based change detection network for bitemporal VHR images S. Pang et al. https://doi.org/10.1080/01431161.2021.1941390
73. From Multiple Independent Metrics to Single Performance Measure Based on Objective Function A. Nandi https://doi.org/10.1109/ACCESS.2023.3235014
74. SDCDNet: A Semi-Dual Change Detection Network Framework With Super-Weak Label for Remote Sensing Image J. Wang et al. https://doi.org/10.1109/TGRS.2023.3286113
75. 用于遥感图像变化检测的结构感知多尺度混合网络 刘. Liu Qi et al. https://doi.org/10.3788/LOP240514
76. MDS-Net: An Image-Text Enhanced Multimodal Dual-Branch Siamese Network for Remote Sensing Change Detection T. Wang et al. https://doi.org/10.1109/JSTARS.2025.3567508
77. CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model S. Liu et al. https://doi.org/10.1109/JSTARS.2025.3559085
78. Change Detection of High-Resolution Remote Sensing Images Through Adaptive Focal Modulation on Hierarchical Feature Maps L. Fazry et al. https://doi.org/10.1109/ACCESS.2023.3292531
79. CDZoom: a human-like sequential zoom agent for efficient change detection in large scenes Y. Lin et al. https://doi.org/10.1007/s00521-022-08096-2
80. Remote sensing image change detection network with multi-scale feature information mining and fusion S. Xue et al. https://doi.org/10.1007/s10044-025-01420-1
81. BiFA: Remote Sensing Image Change Detection With Bitemporal Feature Alignment H. Zhang et al. https://doi.org/10.1109/TGRS.2024.3376673
82. ISNet: Towards Improving Separability for Remote Sensing Image Change Detection G. Cheng et al. https://doi.org/10.1109/TGRS.2022.3174276
83. AANet: An Ambiguity-Aware Network for Remote-Sensing Image Change Detection R. Hang et al. https://doi.org/10.1109/TGRS.2024.3371463
84. A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection Q. Shi et al. https://doi.org/10.1109/TGRS.2021.3085870
85. DWFDA-CD:A Diffusion Model-Based Wavelet Frequency Domain Attention Network for Remote Sensing Change Detection Y. Zhu et al. https://doi.org/10.1109/JSTARS.2025.3609966
86. TransUNet++SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images Y. Du et al. https://doi.org/10.3390/rs15010006
87. AFSNet: Remote sensing change detection with progressive difference feature aggregation and full-scale connection Y. Fu et al. https://doi.org/10.1016/j.eswa.2025.129994
88. Robust Change Detection Using Channel-Wise co-Attention-Based Siamese Network With Contrastive Loss Function E. Choi & J. Kim https://doi.org/10.1109/ACCESS.2022.3170704
89. Hallucination-Resistant Change Detection in Multimodal Large Models for Autonomous Land Management Agents L. Zuo et al. https://doi.org/10.1109/JSTARS.2026.3656403
90. GeoHam: Symplectic-Regularized Feature Evolution for Efficient Remote Sensing Change Detection S. Dabral et al. https://doi.org/10.1109/LGRS.2026.3728830
91. SynerCD: Synergistic Tri-Branch and Vision-Language Coupling for Remote Sensing Change Detection Y. Tong et al. https://doi.org/10.3390/rs17223694
92. Frequency-Domain Heterogeneous Rank-Entropy Bipolarization Network for Remote Sensing Change Detection H. Sun et al. https://doi.org/10.1109/TGRS.2026.3688719
93. MFMENet: multi-scale features mutual enhancement network for change detection in remote sensing images S. Li et al. https://doi.org/10.1080/01431161.2024.2343139
94. EATDer: Edge-Assisted Adaptive Transformer Detector for Remote Sensing Change Detection J. Ma et al. https://doi.org/10.1109/TGRS.2023.3344083
95. Toward Efficient Remote Sensing Image Change Detection via Cross-Temporal Context Learning Z. Song et al. https://doi.org/10.1109/TGRS.2023.3280902
96. CoreNet: A Semi-Supervised Change Detection Framework With Correlation-Guided Feature Refinement and Regional-Wise Pseudo-Label Reassignment T. Lei et al. https://doi.org/10.1109/TGRS.2026.3660884
97. A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning Z. Wang et al. https://doi.org/10.3390/s24051509
98. Change Detection Needs Neighborhood Interaction in Transformer H. Ma et al. https://doi.org/10.3390/rs15235459
99. Cross-Level Attentive Feature Aggregation for Change Detection G. Wang et al. https://doi.org/10.1109/TCSVT.2023.3344092
100. ChangeRD: A registration-integrated change detection framework for unaligned remote sensing images W. Jing et al. https://doi.org/10.1016/j.isprsjprs.2024.11.019
101. FuzzyCD: A Fuzzy Logic-Integrated Network for Robust Remote Sensing Change Detection With Noisy Labels J. Li et al. https://doi.org/10.1109/TGRS.2026.3672449
102. Exchanging Dual-Encoder–Decoder: A New Strategy for Change Detection With Semantic Guidance and Spatial Localization S. Zhao et al. https://doi.org/10.1109/TGRS.2023.3327780
103. Progressive Domain Adaptation for Change Detection Using Season-Varying Remote Sensing Images R. Kou et al. https://doi.org/10.3390/rs12223815
104. A Self-Supervised Learning Pretraining Framework for Remote Sensing Image Change Detection L. Wan et al. https://doi.org/10.1109/TGRS.2025.3579416
105. MCHA-Net: A multi-end composite higher-order attention network guided with hierarchical supervised signal for high-resolution remote sensing image change detection H. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2023.05.033
106. ESR-DMNet: Enhanced Super-Resolution-Based Dual-Path Metric Change Detection Network for Remote Sensing Images With Different Resolutions X. Li et al. https://doi.org/10.1109/TGRS.2024.3362895
107. Frequency-Enhanced Mamba for Remote Sensing Change Detection Y. Xing et al. https://doi.org/10.1109/LGRS.2025.3551754
108. Remote-Sensing Image Change Detection Based on Adjacent-Level Feature Fusion and Dense Skip Connections B. Huang et al. https://doi.org/10.1109/JSTARS.2024.3374290
109. CAS-Net: Comparison-Based Attention Siamese Network for Change Detection With an Open High-Resolution UAV Image Dataset Y. Zhai et al. https://doi.org/10.1109/TGRS.2024.3386918
110. ESIFCD: A Dual-Temporal Cross-Domain Fusion Network for Remote Sensing Change Detection R. Ashraf et al. https://doi.org/10.1109/ACCESS.2025.3612956
111. Adaptive Spatial and Difference Learning for Change Detection Y. Liu et al. https://doi.org/10.1109/JSTARS.2023.3298097
112. Difference Perception Fusion Network for Remote Sensing Image Change Detection L. Wang et al. https://doi.org/10.1109/JSTARS.2025.3584758
113. HeteCD: Feature Consistency Alignment and difference mining for heterogeneous remote sensing image change detection W. Jing et al. https://doi.org/10.1016/j.isprsjprs.2025.03.008
114. Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference X. Peng et al. https://doi.org/10.1109/TGRS.2020.3033009
115. SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection C. Zhang et al. https://doi.org/10.1109/TGRS.2022.3160007
116. ESMII-Net: An edge-synergy and multidimensional information interaction network for remote sensing change detection Y. Chen et al. https://doi.org/10.1016/j.jag.2025.104507
117. FCDNet: A Change Detection Network Based on Full-Scale Skip Connections and Coordinate Attention X. Xiang et al. https://doi.org/10.1109/LGRS.2022.3184179
118. AFSNet: attention-guided full-scale feature aggregation network for high-resolution remote sensing image change detection M. Jiang et al. https://doi.org/10.1080/15481603.2022.2142626
119. DLB-CNet: Difference Learning-Based Convolution Network for Building Change Detection Z. Fan et al. https://doi.org/10.1109/TVLSI.2024.3438728
120. Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China L. Fu et al. https://doi.org/10.3390/rs17132249
121. SCAD-Net: A Semi-Supervised Connectivity-Aware Decoupling Network for High-Resolution Remote Sensing Image Change Detection Y. Zhou et al. https://doi.org/10.1109/TGRS.2025.3627655
122. Boundary Extraction Constrained Siamese Network for Remote Sensing Image Change Detection J. Lei et al. https://doi.org/10.1109/TGRS.2022.3165851
123. Transformer-based multi-scale feature fusion network for remote sensing change detection S. Liang et al. https://doi.org/10.1117/1.JRS.16.046509
124. Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network B. Wang et al. https://doi.org/10.1109/JSTARS.2023.3267482
125. Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection Z. Zheng et al. https://doi.org/10.1007/s11263-024-02141-4
126. Learning From Human Insights for Remote Sensing Change Detection Z. Li et al. https://doi.org/10.1109/TGRS.2025.3642171
127. Relation-Aware High-Order Interaction Network for Remote Sensing Image Change Detection H. Dong et al. https://doi.org/10.1109/TGRS.2025.3604400
128. LGTC: Local-Global Tri-Consistency Network for Semi-Supervised Change Detection of Remote Sensing Images J. Liu et al. https://doi.org/10.1109/TGRS.2025.3619036
129. NF-SemiCD: semi-supervised remote sensing change detection with normalizing flows G. Yu et al. https://doi.org/10.7717/peerj-cs.3751
130. Continuous Cross-Resolution Remote Sensing Image Change Detection H. Chen et al. https://doi.org/10.1109/TGRS.2023.3325829
131. Multiple Attention Siamese Network for High-Resolution Image Change Detection J. Huang et al. https://doi.org/10.1109/TGRS.2021.3127580
132. RDP-Net: Region Detail Preserving Network for Change Detection H. Chen et al. https://doi.org/10.1109/TGRS.2022.3227098
133. PAdaptCD: Progressive Adaptive Thresholding and Bitemporal Image Augmentation for Semisupervised Change Detection L. Wang et al. https://doi.org/10.1109/JSTARS.2025.3586016
134. EarthNets: Empowering artificial intelligence for Earth observation Z. Xiong et al. https://doi.org/10.1109/MGRS.2024.3466998
135. STCD: efficient Siamese transformers-based change detection method for remote sensing images D. Wang et al. https://doi.org/10.1080/10095020.2022.2157762
136. Change Knowledge-Guided Vision-Language Remote Sensing Change Detection J. Wang et al. https://doi.org/10.1109/TGRS.2025.3568521
137. PSNet: change detection with prototype similarity P. Tang et al. https://doi.org/10.1007/s00371-021-02177-4
138. Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity J. Xu et al. https://doi.org/10.3390/rs13153053
139. A Change Detection Method Based on Full-scale Skip Connections and Mixed Pooling Module D. Tian https://doi.org/10.1088/1742-6596/2258/1/012059
140. MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection Y. Cui et al. https://doi.org/10.1109/JSTARS.2025.3570108
141. Change Detection in Remote Sensing Imagery: A Systematic Review of Statistical, Machine Learning, and Deep Learning Methods M. Jabbarizadegan & P. Fraternali https://doi.org/10.3390/rs18152573
142. A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios Y. You et al. https://doi.org/10.3390/rs12152460
143. MCECF: A Multiscale Complementary Enhanced Context Fusion Network for Remote Sensing Change Detection Z. Huang et al. https://doi.org/10.1109/TGRS.2025.3556237
144. Dual-stream hybrid architecture with adaptive multi-scale boundary-aware mechanisms for robust urban change detection in smart cities I. Ahmad et al. https://doi.org/10.1038/s41598-025-16148-5
145. DeepMapper: A GeoAI Approach to Automate the VGI Mapping Workflow L. Niroshan & J. Carswell https://doi.org/10.1109/JSTARS.2025.3581499
146. SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images S. Fang et al. https://doi.org/10.1109/LGRS.2021.3056416
147. A Deep Learning Approach to Extract Internal Tides Scattered by Geostrophic Turbulence H. Wang et al. https://doi.org/10.1029/2022GL099400
148. LRDE-Net: Large Receptive Field and Image Difference Enhancement Network for Remote Sensing Images Change Detection L. Li et al. https://doi.org/10.1109/JSTARS.2023.3326962
149. Sample Inflation Interpolation for Consistency Regularization in Remote Sensing Change Detection Z. Jiang et al. https://doi.org/10.3390/math12223577
150. FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution Remote Sensing Change Detection Z. Xie et al. https://doi.org/10.1109/TGRS.2026.3666124
151. Bitemporal Attention Sharing Network for Remote Sensing Image Change Detection Z. Wang et al. https://doi.org/10.1109/JSTARS.2024.3400925
152. Unsupervised Noise-Resistant Remote-Sensing Image Change Detection: A Self-Supervised Denoising Network-, FCM_SICM-, and EMD Metric-Based Approach J. Xie et al. https://doi.org/10.3390/rs16173209
153. A Position-Temporal Awareness Transformer for Remote Sensing Change Detection Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3432320
154. Performance Improvement of Encoder/Decoder-Based CNN Architectures for Change Detection from Very High-Resolution Satellite Imagery E. Bousias Alexakis & C. Armenakis https://doi.org/10.1080/07038992.2021.1922880
155. SAASNets: Shared attention aggregation Siamese networks for building change detection in multispectral remote sensing S. Pang et al. https://doi.org/10.1371/journal.pone.0306755
156. A Triple-Branch Hybrid Attention Network With Bitemporal Feature Joint Refinement for Remote-Sensing Image Semantic Change Detection H. Chang et al. https://doi.org/10.1109/TGRS.2024.3376384
157. Lightweight Omni-Scale Feature Learning and Alignment Network for Remote Sensing Image Change Detection J. Li et al. https://doi.org/10.1109/LGRS.2026.3657666
158. Unsupervised Bayesian change detection for remotely sensed images W. Gharbi et al. https://doi.org/10.1007/s11760-020-01738-9
159. MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion S. Jiang et al. https://doi.org/10.3390/rs16081387
160. Hyperboloid-Embedded Siamese Network for Change Detection in Remote Sensing Images Q. Yang et al. https://doi.org/10.1109/JSTARS.2024.3390427
161. StyleDe-CD: Dual-Level Style Disentanglement for Semisupervised Change Detection Z. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3683164
162. A Survey on the Datasets and Algorithms for Satellite Data Applications M. Affek & J. Szymański https://doi.org/10.1109/JSTARS.2024.3424954
163. TransC-GD-CD: Transformer-Based Conditional Generative Diffusion Change Detection Model Y. Wen et al. https://doi.org/10.1109/JSTARS.2024.3373201
164. Change Detection With Cross-Domain Remote Sensing Images: A Systematic Review J. Chen et al. https://doi.org/10.1109/JSTARS.2024.3416183
165. Remote Sensing Change Detection With Bitemporal and Differential Feature Interactive Perception H. Chang et al. https://doi.org/10.1109/TIP.2024.3424335
166. Dynamically Updated Semi-Supervised Change Detection Network Combining Cross-Supervision and Screening Algorithms S. Yuan et al. https://doi.org/10.1109/TGRS.2024.3369059
167. DPCC-Net: Dual-perspective change contextual network for change detection in high-resolution remote sensing images Q. Shu et al. https://doi.org/10.1016/j.jag.2022.102940
168. HFIFNet: Hierarchical Feature Interaction Network With Multiscale Fusion for Change Detection M. Han et al. https://doi.org/10.1109/JSTARS.2025.3528053
169. Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks S. Chang et al. https://doi.org/10.1109/TGRS.2024.3424532
170. Attention-Guided Generative Adversarial Network for False Alarm-Resistant Change Detection in Remote Sensing Orthophotos Y. Hu et al. https://doi.org/10.3390/rs18142290
171. MSSM-SCDNet: A Multiclass Semantic Change Detection Network Suitable for Coastal Areas Based on Multiband Spatial-spectral Attention Mechanism Z. Liu et al. https://doi.org/10.1109/JSTARS.2024.3422901
172. MCSD-CD: Resolution-aware scale space Mamba with Cross-Scan modeling for change detection J. Singh et al. https://doi.org/10.1016/j.ophoto.2026.100156
173. DepthCD: Depth prompting in 2D remote sensing imagery change detection N. Zhou et al. https://doi.org/10.1016/j.isprsjprs.2025.05.020
174. From Stochasticity to Stability: A Monte Carlo-Guided Generative Prior Paradigm for Remote Sensing Change Detection Y. Liao et al. https://doi.org/10.1109/TGRS.2026.3717664
175. EDG-Net: A Lightweight Frequency-Aware CNN-Transformer Hybrid Network for Efficient Remote Sensing Change Detection Q. Meng et al. https://doi.org/10.1109/JSTARS.2026.3722602
176. Change Detection Based on Supervised Contrastive Learning for High-Resolution Remote Sensing Imagery J. Wang et al. https://doi.org/10.1109/TGRS.2023.3236664
177. Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network Y. Zhang et al. https://doi.org/10.3390/s20236735
178. Robust feature aggregation network for lightweight and effective remote sensing image change detection Z. You et al. https://doi.org/10.1016/j.isprsjprs.2024.06.013
179. NestNet: a multiscale convolutional neural network for remote sensing image change detection X. Yu et al. https://doi.org/10.1080/01431161.2021.1906982
180. A Siamese Swin-Unet for image change detection Y. Tang et al. https://doi.org/10.1038/s41598-024-54096-8
181. Efficient Remote Sensing Change Detection With Change State Space Models E. Ghazaei & E. Aptoula https://doi.org/10.1109/LGRS.2025.3629303
182. Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images L. Fazry et al. https://doi.org/10.1109/ACCESS.2025.3580481
183. Pretrain a Remote Sensing Foundation Model by Promoting Intra-Instance Similarity X. An et al. https://doi.org/10.1109/TGRS.2024.3469568
184. WRSI: Wavelet scale reduction and spatiotemporal boundary interaction network for remote sensing change detection K. Li et al. https://doi.org/10.1016/j.ins.2025.123002
185. En-Decoded Index Guided Edge Refinement Network for Change Detection of Remote Sensing Image C. Yu et al. https://doi.org/10.1109/JSTARS.2025.3585308
186. SCAFNet: A Semantic Compensated Adaptive Fusion Network for Remote Sensing Images Change Detection Y. Zhang et al. https://doi.org/10.1109/LGRS.2025.3650414
187. Multilevel feature aggregation and enhancement network for remote sensing change detection W. Yan et al. https://doi.org/10.1117/1.JRS.18.016513
188. Lightweight liquid siamese neural network for robust multimodal satellite image change detection S. Kasetty & K. Rajakumar https://doi.org/10.1038/s41598-026-45655-2
189. Fully convolutional siamese networks based change detection for optical aerial images with focal contrastive loss Z. Wang et al. https://doi.org/10.1016/j.neucom.2021.06.059
190. MSFF-CDNet: A Multiscale Feature Fusion Change Detection Network for Bi-Temporal High-Resolution Remote Sensing Image L. Wang et al. https://doi.org/10.1109/LGRS.2023.3305623
191. A Fractal Projection and Markovian Segmentation-Based Approach for Multimodal Change Detection M. Mignotte https://doi.org/10.1109/TGRS.2020.2986239
192. Bio-inspired Multi-Sensory Pathway Network for Change Detection K. Liu & X. Li https://doi.org/10.1007/s12559-021-09968-w
193. A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images M. Yin et al. https://doi.org/10.3390/rs15092406
194. Asymmetric Cross-Attention Hierarchical Network Based on CNN and Transformer for Bitemporal Remote Sensing Images Change Detection X. Zhang et al. https://doi.org/10.1109/TGRS.2023.3245674
195. A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images Z. Li et al. https://doi.org/10.1109/TGRS.2022.3159544
196. A Temporal-Reliable Method for Change Detection in High-Resolution Bi-Temporal Remote Sensing Images F. Pan et al. https://doi.org/10.3390/rs14133100
197. A Multiscale Cascaded Cross-Attention Hierarchical Network for Change Detection on Bitemporal Remote Sensing Images X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3361847
198. Lightweight Structure-Aware Transformer Network for Remote Sensing Image Change Detection T. Lei et al. https://doi.org/10.1109/LGRS.2023.3323534
199. Iterative Mamba Diffusion Change-Detection Model for Remote Sensing F. Liu et al. https://doi.org/10.3390/rs16193651
200. DED-SAM:Adapting Segment Anything Model 2 for Dual Encoder–Decoder Change Detection J. Qiu et al. https://doi.org/10.1109/JSTARS.2024.3490754
201. AMCA: Attention-Guided Multiscale Context Aggregation Network for Remote Sensing Image Change Detection X. Xu et al. https://doi.org/10.1109/TGRS.2023.3272006
202. Semantic change detection of roads and bridges: A fine-grained dataset and multimodal frequency-driven detector Q. Shu et al. https://doi.org/10.1016/j.patcog.2026.113191
203. Foggy-UAVCD and DSRF-Net: Benchmarking robust satellite–UAV change detection under fog degradation X. Huang et al. https://doi.org/10.1016/j.isprsjprs.2026.08.026
204. Dual-Branch Seasonal Error Elimination Change Detection Framework Using Target Image Feature Fusion Generator H. Zhu et al. https://doi.org/10.3390/rs17030523
205. Difference Enhancement and Spatial–Spectral Nonlocal Network for Change Detection in VHR Remote Sensing Images T. Lei et al. https://doi.org/10.1109/TGRS.2021.3134691
206. Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images M. Lin et al. https://doi.org/10.1109/TIP.2022.3226418
207. Globally-Aware Continuous-Time Redistribution Learning for RS Image Change Detection X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3454055
208. Hardware-efficient satellite change detection via hybrid Mamba decoder with differentiable lookup table classifier and progressive knowledge distillation M. Etemadinia & S. Sharifian https://doi.org/10.1016/j.compeleceng.2026.111507
209. A hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images H. Wu et al. https://doi.org/10.1049/ipr2.13037
210. SFCF-Net: A Cross-Hierarchical Progressive Feature Integration Network for UAV Image Change Detection T. Wu et al. https://doi.org/10.1109/JSTARS.2026.3664258
211. DistillSemiCD: DINOv2-Driven Distillation for Semisupervised Remote Sensing Change Detection Y. Xu et al. https://doi.org/10.1109/TGRS.2026.3711116
212. GSSR-Net: Geo-Spatial Structural Refinement Network for Remote Sensing Change Detection S. Wang et al. https://doi.org/10.1109/TGRS.2025.3555959
213. Automatic Generation Method for Urban Change Detection Dataset J. Ha et al. https://doi.org/10.1109/ACCESS.2026.3659083
214. Semi-Supervised Change Detection With Fourier-Based Frequency Transformation Z. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3414452
215. Fusion of UNet and ResNet decisions for change detection using low and high spectral resolution images E. Brahim et al. https://doi.org/10.1007/s11760-024-03185-2
216. FDFF-Net: A Full-Scale Difference Feature Fusion Network for Change Detection in High-Resolution Remote Sensing Images F. Gu et al. https://doi.org/10.1109/JSTARS.2023.3335287
217. TriFusion-CD: Tri-Source Fusion for Robust Remote Sensing Change Detection Under Pseudo-Change Interference J. Wang et al. https://doi.org/10.3390/rs18101572
218. Global–Local Feature Collaborative Fusion and Edge Refinement Network for Remote Sensing Change Detection S. Li et al. https://doi.org/10.1109/TGRS.2025.3584115
219. GaMPF: A Full-Scale Gated Message Passing Framework Based on Collaborative Estimation for VHR Remote Sensing Image Change Detection X. Zhao et al. https://doi.org/10.1109/TGRS.2023.3344948
220. Multiscale Edge Enhancement and Progressive Change-Aware Network for Remote Sensing Change Detection Y. Xing et al. https://doi.org/10.1109/JSTARS.2025.3584959
221. SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images A. Mohammadian & F. Ghaderi https://doi.org/10.1080/01431161.2023.2225228
222. A Gated Feature Fusion Network With Meta-Encoder and Self-Calibrating Cross Module for Building Change Detection in Remote Sensing Images J. Deng et al. https://doi.org/10.1109/TGRS.2024.3495662
223. Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review P. Chen et al. https://doi.org/10.1109/JSTARS.2025.3555567
224. AGCDetNet:An Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images K. Song & J. Jiang https://doi.org/10.1109/JSTARS.2021.3077545
225. ADHR-CDNet: Attentive Differential High-Resolution Change Detection Network for Remote Sensing Images X. Zhang et al. https://doi.org/10.1109/TGRS.2022.3221492
226. CSD-Net: Content–Style Decoupling with Exploratory MLLM-Guided Refinement for Robust Change Detection B. Peng et al. https://doi.org/10.3390/rs18132074
227. Lighter and Robust: A Rotation-Invariant Transformer for VHR Image Change Detection L. Sun et al. https://doi.org/10.1109/TGRS.2024.3381971
228. DHCNet: Dynamic Hierarchical Collaborative Learning for Change Detection in High-Resolution Remote Sensing Images W. Chen et al. https://doi.org/10.1109/TGRS.2026.3686460
229. Deeper Multiscale Encoding–Decoding Feature Fusion Network for Change Detection of VHR Images J. Yang et al. https://doi.org/10.1109/LGRS.2023.3330867
230. Semantic-Aware Remote Sensing Change Detection with Multi-Scale Cross-Attention X. Zheng et al. https://doi.org/10.3390/s25092813
231. Deep Learning for High-Resolution Remote Sensing Change Detection: Task Taxonomy, Datasets, and Perspectives Y. Chen et al. https://doi.org/10.1109/JSTARS.2025.3622204
232. SUMLP: A Siamese U-shaped MLP-based network for change detection C. Zhang et al. https://doi.org/10.1016/j.asoc.2022.109766
233. Remote Sensing Image Change Detection Combined With Saliency H. Zhang et al. https://doi.org/10.1109/JSEN.2024.3390674
234. Progressive Self-Optimization Network: An unsupervised change detection method for VHR optical remote sensing imagery Y. Shen et al. https://doi.org/10.1016/j.jag.2025.104792
235. Multi-scale graph reasoning network for remote sensing image change detection Y. Shangguan et al. https://doi.org/10.1080/01431161.2023.2221797
236. MAGMamba-CD: A Lightweight Multiscale Adaptive Gated Mamba Network for Remote Sensing Change Detection W. Yue et al. https://doi.org/10.1109/LGRS.2026.3734366
237. LFHNet: Lightweight Full-Scale Hybrid Network for Remote Sensing Change Detection X. Jiang et al. https://doi.org/10.1109/JSTARS.2024.3400458
238. DAFNet: A Novel Change-Detection Model for High-Resolution Remote-Sensing Imagery Based on Feature Difference and Attention Mechanism C. Ma et al. https://doi.org/10.3390/rs15153896
239. LWCCNet: Lightweight Remote Sensing Change Detection Network Based on Composite Convolution Operator L. Wang et al. https://doi.org/10.1109/JSTARS.2023.3314133
240. MDINet: Multidomain Incremental Network for Change Detection L. Weng et al. https://doi.org/10.1109/TGRS.2023.3348878
241. Building Instance Change Detection from High Spatial Resolution Remote Sensing Images Using Improved Instance Segmentation Architecture L. Yan et al. https://doi.org/10.1007/s12524-022-01601-z
242. Enhanced Feature Interaction Network for Remote Sensing Change Detection S. Liang et al. https://doi.org/10.1109/LGRS.2023.3319648
243. Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images R. Patra et al. https://doi.org/10.3390/rs14215402
244. STAR-CD: Style-Aligned Remote Sensing Change Detection With Appearance-Relation Modeling Q. Hu et al. https://doi.org/10.1109/TGRS.2025.3643487
245. Difference Enhancement and Dependence-Aware Network for Remote Sensing Image Change Detection H. Li et al. https://doi.org/10.1109/JSTARS.2025.3599669
246. A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection W. Wang et al. https://doi.org/10.1109/JSTARS.2022.3198517
247. DASUNet: a deeply supervised change detection network integrating full-scale features R. Miao et al. https://doi.org/10.1038/s41598-024-63257-8
248. A feature enhancement network combining UNet and vision transformer for building change detection in high-resolution remote sensing images Y. Sun et al. https://doi.org/10.1007/s00521-024-10666-5
249. CSTSUNet: A Cross Swin Transformer-Based Siamese U-Shape Network for Change Detection in Remote Sensing Images Y. Wu et al. https://doi.org/10.1109/TGRS.2023.3326813
250. Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images E. Adil et al. https://doi.org/10.3390/rs14246361
251. Siamese change detection based on information interaction and fusion network Y. Zhang et al. https://doi.org/10.1038/s41598-025-15468-w
252. IPACN: Information-Preserving Adaptive Convolutional Network for Remote Sensing Change Detection H. Qi et al. https://doi.org/10.3390/rs17132121
253. UDCMamba: Uncertainty-Driven Change Detection for Remote Sensing With State-Space Model Q. Gao et al. https://doi.org/10.1109/LGRS.2026.3705638
254. MFGFNet: A Multi-Scale Remote Sensing Change Detection Network Using the Global Filter in the Frequency Domain S. Yuan et al. https://doi.org/10.3390/rs15061682
255. Improving change detection using conditional discriminative adversarial regularization L. Wang et al. https://doi.org/10.1007/s00138-025-01769-7
256. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities L. Zhang & L. Zhang https://doi.org/10.1109/MGRS.2022.3145854
257. Multi-Level Difference Network for Change Detection from Very High-Resolution Remote Sensing Images: A Case Study in Open-Pit Mines W. Li et al. https://doi.org/10.3390/rs15143482
258. SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection J. Wang & C. Wu https://doi.org/10.3390/rs16071274
259. TINYCD: a (not so) deep learning model for change detection A. Codegoni et al. https://doi.org/10.1007/s00521-022-08122-3
260. A Novel Transformer-Based Multiscale Siamese Framework for High-Resolution Remote Sensing Change Detection L. Wang et al. https://doi.org/10.1109/JSTARS.2025.3569104
261. Efficient Adjacent Feature Harmonizer Network With UAV-CD+ Dataset for Remote Sensing Change Detection Y. Zhai et al. https://doi.org/10.1109/TGRS.2024.3502768
262. Multiscale Context Aggregation Network for Building Change Detection Using High Resolution Remote Sensing Images J. Dong et al. https://doi.org/10.1109/LGRS.2021.3121094
263. AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluation L. Ran et al. https://doi.org/10.1109/TGRS.2025.3551504
264. SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images D. Peng et al. https://doi.org/10.1109/TGRS.2020.3011913
265. Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images T. Lei et al. https://doi.org/10.1109/TGRS.2023.3261273
266. Self-Supervised Pre-Training Style Adaptation-Guided Remote Sensing Image Change Detection Network B. Zhang et al. https://doi.org/10.3390/rs18152523
267. MFIHNet: Multiscale Feature Interaction Hybrid Network for Change Detection of Remote Sensing Images L. Cao et al. https://doi.org/10.1109/JSTARS.2024.3450287
268. DSFI-CD: Diffusion-Guided Spatial-Frequency-Domain Information Interaction for Remote Sensing Image Change Detection X. Li et al. https://doi.org/10.1109/TGRS.2025.3544402
269. A differential auxiliary semantic interactive network for high-resolution remote sensing image change detection Y. Zhao et al. https://doi.org/10.1016/j.ecoinf.2025.103321
270. SSLChange: A Self-Supervised Change Detection Framework Based on Domain Adaptation Y. Zhao et al. https://doi.org/10.1109/TGRS.2024.3489615
271. GCCANet: A Small-Object Sensitive and Environment-Robust Network for Remote Sensing Change Detection J. Chen et al. https://doi.org/10.1109/TGRS.2026.3720341
272. Resolution-Difference Embedded Network for Cross-Resolution Remote Sensing Image Change Detection G. Wang et al. https://doi.org/10.1109/TGRS.2025.3600397
273. EMS-CDNet: an efficient multi-scale-fusion change detection network for very high-resolution remote sensing images Z. Zheng et al. https://doi.org/10.1080/01431161.2022.2131479
274. Semisupervised Change Detection With Feature-Prediction Alignment X. Zhang et al. https://doi.org/10.1109/TGRS.2023.3247605
275. TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote-Sensing Images Q. Li et al. https://doi.org/10.1109/TGRS.2022.3169479
276. RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation H. Bi et al. https://doi.org/10.1109/TPAMI.2025.3643453
277. A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning W. Yang et al. https://doi.org/10.1155/2022/3404858
278. Multi-scale feature progressive fusion network for remote sensing image change detection D. Lu et al. https://doi.org/10.1038/s41598-022-16329-6
279. IRA-MRSNet: A Network Model for Change Detection in High-Resolution Remote Sensing Images J. Ling et al. https://doi.org/10.3390/rs14215598
280. HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images H. Li et al. https://doi.org/10.3390/s22124626
281. Harmony in diversity: Content cleansing change detection framework for very-high-resolution remote-sensing images M. Cheng et al. https://doi.org/10.1016/j.isprsjprs.2024.09.002
282. A Siamese Network with a Multiscale Window-Based Transformer via an Adaptive Fusion Strategy for High-Resolution Remote Sensing Image Change Detection C. Tao et al. https://doi.org/10.3390/rs15092433
283. ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning S. Dong et al. https://doi.org/10.1016/j.isprsjprs.2024.01.004
284. Cross-Image Feature Interaction Network for Change Detection in Remote Sensing Images X. Han et al. https://doi.org/10.3390/rs18050717
285. Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas R. Wan et al. https://doi.org/10.1109/ACCESS.2024.3350641
286. Semantic-aware transformer with feature integration for remote sensing change detection P. Li et al. https://doi.org/10.1016/j.engappai.2024.108774
287. Feature alignment and refinement for Remote Sensing images change Detection Y. Liu et al. https://doi.org/10.1080/01431161.2023.2288949
288. HASNet: A Foreground Association-Driven Siamese Network With Hard Sample Optimization for Remote Sensing Image Change Detection C. Tao et al. https://doi.org/10.1109/TGRS.2025.3545760
289. GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature-Guided DDPM Y. Wen et al. https://doi.org/10.1109/TGRS.2024.3381752
290. Change Detection for High-Resolution Remote Sensing Images Based on a Multi-Scale Attention Siamese Network J. Li et al. https://doi.org/10.3390/rs14143464
291. Domain Adaptive and Interactive Differential Attention Network for Remote Sensing Image Change Detection Y. Ji et al. https://doi.org/10.1109/TGRS.2024.3382116
292. Exploring Image Generation for UAV Change Detection X. Li et al. https://doi.org/10.1109/JAS.2022.105629
293. Frequency-Decoupled Enhancement and Large Kernel Cross-Scale Fusion Network for Remote Sensing Change Detection Y. Xing et al. https://doi.org/10.1109/JSTARS.2026.3726753
294. A Transformer-Based Multiscale Difference Enhancement Network for Change Detection M. Pan et al. https://doi.org/10.1109/JSTARS.2025.3539416
295. DCFF-Net: A Densely Connected Feature Fusion Network for Change Detection in High-Resolution Remote Sensing Images F. Pan et al. https://doi.org/10.1109/JSTARS.2021.3129318
296. Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms L. Wang et al. https://doi.org/10.3390/rs16050804
297. Learning Token-Aligned Representations With Multimodel Transformers for Different-Resolution Change Detection M. Liu et al. https://doi.org/10.1109/TGRS.2022.3200684
298. Super-Resolution-Based Change Detection Network With Stacked Attention Module for Images With Different Resolutions M. Liu et al. https://doi.org/10.1109/TGRS.2021.3091758
299. LGMM-Net: A Local–Global Encoder and Mask Mamba Decoder Network for Remote Sensing Change Detection C. Fang et al. https://doi.org/10.1109/TGRS.2026.3662322
300. VisionTwinNet: Gated Clarity Enhancement Paired With Light-Robust CD Transformers T. Chen & A. Chen https://doi.org/10.1109/ACCESS.2024.3350173
301. 3DGS-CD: 3D Gaussian Splatting-Based Change Detection for Physical Object Rearrangement Z. Lu et al. https://doi.org/10.1109/LRA.2025.3533457
302. MDFANet: Multidimensional Feature Alignment Network for Self-Supervised Pretraining in Remote Sensing Change Detection L. Wang et al. https://doi.org/10.1109/JSTARS.2025.3644606
303. A communication-efficient distributed deep learning remote sensing image change detection framework H. Cheng et al. https://doi.org/10.1016/j.jag.2024.103840
304. MAMI-CD: Multistage Attention Network for Change Detection With Mixed Feature Interaction M. Han et al. https://doi.org/10.1109/JSTARS.2024.3445908
305. Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images F. Zhou et al. https://doi.org/10.1109/JSTARS.2025.3526795
306. Remote sensing image change detection using deep learning techniques: a comprehensive survey T. Lei et al. https://doi.org/10.1007/s10462-026-11501-0
307. Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks P. de Bem et al. https://doi.org/10.3390/rs12060901
308. WNet: W-Shaped Hierarchical Network for Remote-Sensing Image Change Detection X. Tang et al. https://doi.org/10.1109/TGRS.2023.3296383
309. SS-GDAFNet: A Global-Guided Spatial–Frequency Collaborative Network for High-Resolution Remote Sensing Change Detection C. Qiao et al. https://doi.org/10.1109/LGRS.2026.3715020
310. Change Detection Meets Frequency Learning: A Coarse-to-Fine Dual-Domain Detection Network W. Zhang et al. https://doi.org/10.1109/TGRS.2025.3549818
311. TCIANet: Transformer-Based Context Information Aggregation Network for Remote Sensing Image Change Detection X. Xu et al. https://doi.org/10.1109/JSTARS.2023.3241157
312. Joint Self-Training and Rebalanced Consistency Learning for Semi-Supervised Change Detection X. Zhang et al. https://doi.org/10.1109/TGRS.2023.3314452
313. An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing images Y. Sun et al. https://doi.org/10.1016/j.acags.2024.100210
314. DTT-CGINet: A Dual Temporal Transformer Network with Multi-Scale Contour-Guided Graph Interaction for Change Detection M. Chen et al. https://doi.org/10.3390/rs16050844
315. MGC: MLP-Guided CNN Pretraining Using a Small-Scale Dataset for Remote Sensing Images Z. Li et al. https://doi.org/10.1109/TGRS.2024.3407824
316. Learning Remote Sensing Aleatoric Uncertainty for Semi-Supervised Change Detection J. Shen et al. https://doi.org/10.1109/TGRS.2024.3437250
317. MixCDNet: A Lightweight Change Detection Network Mixing Features Across CNN and Transformer L. Wang et al. https://doi.org/10.1109/TGRS.2024.3438228
318. Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection C. Hu et al. https://doi.org/10.1109/ACCESS.2025.3571463
319. DFSMCG-Net: A Siamese change detection network based on Differential Feature Selection and Multi-Scale Guidance Strategies H. Xue et al. https://doi.org/10.1016/j.asoc.2025.113372
320. Remote Sensing Image Change Detection With Transformers H. Chen et al. https://doi.org/10.1109/TGRS.2021.3095166
321. DIAFNet: A Dynamic Interactive Adaptive Fusion Network Based on Enhanced Differential Features R. Zhao et al. https://doi.org/10.1109/LGRS.2024.3359719
322. SIAM-CDNET: A Remote Sensing Image Change Detection Network for Optimized Edge Detection and Mitigation of Pseudo Changes Y. Zhang & W. Xue https://doi.org/10.1109/ACCESS.2024.3451473
323. CANet: A Combined Attention Network for Remote Sensing Image Change Detection D. Lu et al. https://doi.org/10.3390/info12090364
324. xLSTM Interaction Multilevel SSM-Assisted Decoding Network for Remote Sensing Image Change Detection C. Wu et al. https://doi.org/10.1109/JSTARS.2025.3590257
325. Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning J. Wei et al. https://doi.org/10.3390/rs17050798
326. CSIF-CD: A Unified Cross-Stage Interactive Feedback Framework for Remote Sensing Image Change Detection R. Yu et al. https://doi.org/10.1109/LGRS.2026.3697223
327. A Review of Deep-Learning Methods for Change Detection in Multispectral Remote Sensing Images E. Parelius https://doi.org/10.3390/rs15082092
328. Change Detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study V. Kristollari & V. Karathanassi https://doi.org/10.1109/ACCESS.2022.3161978
329. AERNet: An Attention-Guided Edge Refinement Network and a Dataset for Remote Sensing Building Change Detection J. Zhang et al. https://doi.org/10.1109/TGRS.2023.3300533
330. ROFANet: Residual Offset-Driven Feature Alignment Network for Unaligned Remote Sensing Image Change Detection G. Wang et al. https://doi.org/10.1109/JSTARS.2025.3639607
331. SAAN: Similarity-Aware Attention Flow Network for Change Detection With VHR Remote Sensing Images H. Guo et al. https://doi.org/10.1109/TIP.2024.3349868
332. Local Similarity Siamese Network for Urban Land Change Detection on Remote Sensing Images H. Lee et al. https://doi.org/10.1109/JSTARS.2021.3069242
333. MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images Y. Li et al. https://doi.org/10.3390/rs16183466
334. Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images H. Chen et al. https://doi.org/10.1109/TGRS.2021.3066802
335. EUNet-CD: Efficient UNet++ for Change Detection of Very High-Resolution Remote Sensing Images A. Raza et al. https://doi.org/10.1109/LGRS.2022.3144304
336. AM-CD: Joint attention and Mamba for remote sensing image change detection Y. Guo et al. https://doi.org/10.1016/j.neucom.2025.130607
337. BASNet: A Boundary-Aware Siamese Network for Accurate Remote-Sensing Change Detection H. Wei et al. https://doi.org/10.1109/LGRS.2021.3119885
338. Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods U. Marmol & N. Borowiec https://doi.org/10.3390/rs15051414
339. Change detection of multisource remote sensing images: a review W. Jiang et al. https://doi.org/10.1080/17538947.2024.2398051
340. MLGA-Mamba: Multiscale Locally Gated Attention and Dual-Stream Decoder Guided Vision Mamba for Change Detection of Fast-Growing Urban Regions F. Song et al. https://doi.org/10.1109/JSTARS.2026.3700090
341. Network and Dataset for Multiscale Remote Sensing Image Change Detection S. Liu et al. https://doi.org/10.1109/JSTARS.2024.3522135
342. Toward generalizing to unseen domains in remote sensing: A survey M. Liao et al. https://doi.org/10.1016/j.neucom.2026.134238
343. DCAT: Dual Cross-Attention-Based Transformer for Change Detection Y. Zhou et al. https://doi.org/10.3390/rs15092395
344. TDNet: Bidirectional LSTM Gated Triple-Decoder Network for Remote Sensing Change Detection C. Zhang et al. https://doi.org/10.1109/TGRS.2025.3644652
345. EfficientViM-CD: An Efficient Remote Sensing Change Detection Network Based on Hidden-State-Mixer H. Fan et al. https://doi.org/10.1109/JSTARS.2026.3656676
346. Remote Sensing Spatiotemporal Vision–Language Models: A comprehensive survey C. Liu et al. https://doi.org/10.1109/MGRS.2025.3598283
347. Uncertainty-Guided Semisupervised Change Detection for Remote Sensing Images Y. Gao et al. https://doi.org/10.1109/TGRS.2026.3710667
348. GCMamba: A Mamba-Based Network With Graph Attention and Contrastive Learning for Remote Sensing Image Change Detection B. Fu et al. https://doi.org/10.1109/JSTARS.2026.3695651
349. Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey L. Jiao et al. https://doi.org/10.1109/JSTARS.2023.3247455
350. Remote Sensing Image Change Detection Based on Lightweight Transformer and Multiscale Feature Fusion J. Li et al. https://doi.org/10.1109/JSTARS.2025.3529529
351. HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images Y. Zhang et al. https://doi.org/10.3390/rs13081440
352. JL1-CD: A New Benchmark and a Robust Multiteacher Knowledge Distillation Framework for Remote Sensing Change Detection Z. Liu et al. https://doi.org/10.1109/TIM.2026.3670598
353. ViTs-based Dual Metric Deep Learning Technique for change detection from high-resolution satellite images Y. Afaq & N. Koufi https://doi.org/10.1016/j.rsase.2026.101956
354. Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection W. Zhao et al. https://doi.org/10.1109/TGRS.2019.2953879
355. CSDACD: Domain-Adaptive Change Detection Network for Cross-Seasonal Remote Sensing Images Y. Huang & P. Zhang https://doi.org/10.1109/LGRS.2024.3431212
356. MATNet: Multilevel attention-based transformers for change detection in remote sensing images Z. Zhang et al. https://doi.org/10.1016/j.imavis.2024.105294
357. A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images H. Jiang et al. https://doi.org/10.3390/rs14071552
358. A Combined Loss-Based Multiscale Fully Convolutional Network for High-Resolution Remote Sensing Image Change Detection X. Li et al. https://doi.org/10.1109/LGRS.2021.3098774
359. Developing approaches in building classification and extraction with synergy of YOLOV8 and SAM models A. Khatua et al. https://doi.org/10.1007/s41324-024-00574-0
360. HMCNet: Hybrid Efficient Remote Sensing Images Change Detection Network Based on Cross-Axis Attention MLP and CNN L. Wang & H. Li https://doi.org/10.1109/TGRS.2022.3215244
361. Cross attention is all you need: relational remote sensing change detection with transformer K. Lu et al. https://doi.org/10.1080/15481603.2024.2380126
362. EMINet-CD: An Edge-Enhanced Mixed Information Interaction Network for Remote Sensing Image Change Detection Z. Lyu et al. https://doi.org/10.1109/JSTARS.2026.3712553
363. Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya R. Shults et al. https://doi.org/10.3390/rs17040616
364. Exploring the Cross-Temporal Interaction: Feature Exchange and Enhancement for Remote Sensing Change Detection Y. Liu et al. https://doi.org/10.1109/JSTARS.2024.3413715
365. DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid X. Zhang et al. https://doi.org/10.1109/LGRS.2021.3049370
366. A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images J. Wu et al. https://doi.org/10.3390/rs15010045
367. ECPS: Cross Pseudo Supervision Based on Ensemble Learning for Semi-Supervised Remote Sensing Change Detection Y. Yang et al. https://doi.org/10.1109/TGRS.2024.3370236
368. A hybrid CNN-transformer network with difference enhancement and frequency fusion for remote sensing image change detection M. Wang et al. https://doi.org/10.1007/s10489-025-07034-8
369. MFATNet: Multi-Scale Feature Aggregation via Transformer for Remote Sensing Image Change Detection Z. Mao et al. https://doi.org/10.3390/rs14215379
370. D-TNet: Category-Awareness Based Difference-Threshold Alternative Learning Network for Remote Sensing Image Change Detection L. Wan et al. https://doi.org/10.1109/TGRS.2022.3213925
371. StyleFormer: Spatial–Temporal Style Projecting Bidirectional Interactive Transformer for Change Detection Q. Han et al. https://doi.org/10.1109/TGRS.2025.3535496
372. CSA-CDGAN: channel self-attention-based generative adversarial network for change detection of remote sensing images Z. Wang et al. https://doi.org/10.1007/s00521-022-07637-z
373. MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images W. Ren et al. https://doi.org/10.3390/rs16071269
374. Content-Invariant Dual Learning for Change Detection in Remote Sensing Images B. Fang et al. https://doi.org/10.1109/TGRS.2021.3064501
375. Remote Sensing Image Change Detection Based on Deep Multi-Scale Multi-Attention Siamese Transformer Network M. Zhang et al. https://doi.org/10.3390/rs15030842
376. MFNet: Mutual Feature-Aware Networks for Remote Sensing Change Detection Q. Zhang et al. https://doi.org/10.3390/rs15082145
377. Laplacian Pyramid Network With Hybrid Encoder and Edge Guidance for Remote Sensing Change Detection W. Yan et al. https://doi.org/10.1109/JSTARS.2024.3491762
378. A Deep Supervised Change Detection Network Based on Context-Rich Information Y. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3601214
379. Difference Enhancement and Interscale Interactive Fusion Mamba for Remote Sensing Image Change Detection W. Sun et al. https://doi.org/10.1109/TGRS.2025.3628639
380. A practically feasible transfer learning method for deep-temporal urban change monitoring G. Zitzlsberger et al. https://doi.org/10.1080/01431161.2023.2243021
381. Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data T. Dedring & A. Rienow https://doi.org/10.1080/15481603.2024.2364460
382. DSQNet: Enhancing Change Detection Network via Deep Semantics Query for Remote Sensing Images Z. Qin & L. Zhang https://doi.org/10.1109/LGRS.2024.3474181
383. SKYDET: An End-to-End Multiscale Attentive Detection Network From Foundation Models for Small Objects in Remote Sensing Images Y. Zhang et al. https://doi.org/10.1109/TGRS.2026.3716766
384. DAChanger: A remote sensing change detector inspired by differential amplifier circuit for signal energy Decoupling and geometric consistency P. Chen et al. https://doi.org/10.1016/j.isprsjprs.2026.07.021
385. CDHNet: An implicit calibration and difference-guided hierarchical perception change detection network for remote sensing images H. Cheng et al. https://doi.org/10.1016/j.isprsjprs.2026.06.040
386. ESCNet: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images H. Zhang et al. https://doi.org/10.1109/TNNLS.2021.3089332
387. A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection R. Zhou et al. https://doi.org/10.1109/TGRS.2023.3344751
388. Remote Sensing Change Detection With Transformers Trained From Scratch M. Noman et al. https://doi.org/10.1109/TGRS.2024.3383800
389. ConvTransNet: A CNN–Transformer Network for Change Detection With Multiscale Global–Local Representations W. Li et al. https://doi.org/10.1109/TGRS.2023.3272694
390. SAM-Driven Consistency Regulation for Semi-Supervised Remote Sensing Change Detection L. Lv et al. https://doi.org/10.1109/TGRS.2026.3674069
391. Uncertainty-Aware Exchange Consistency Learning for Robust Semi-Supervised Remote Sensing Change Detection H. Chen et al. https://doi.org/10.1109/TGRS.2026.3709700
392. SCEDNet: A Style Consistency Enhanced Differential Network for Remote Sensing Image Change Detection Z. Yang et al. https://doi.org/10.1109/LGRS.2025.3574734
393. An Efficient Lightweight Neural Network for Remote Sensing Image Change Detection K. Song et al. https://doi.org/10.3390/rs13245152
394. An Object Fine-Grained Change Detection Method Based on Frequency Decoupling Interaction for High-Resolution Remote Sensing Images Y. Tang et al. https://doi.org/10.1109/TGRS.2023.3337816
395. Beyond Cross-Temporal Difference: Style-Aligned and Fusion-Difference Learning for Change Detection S. Fu et al. https://doi.org/10.1109/TGRS.2025.3583166
396. Self-Attention and Convolution Fusion Network for Land Cover Change Detection over a New Data Set in Wenzhou, China Y. Zhu et al. https://doi.org/10.3390/rs14235969
397. Uncertainty-Aware Noisy Label Learning for Remote Sensing Change Detection B. Yang et al. https://doi.org/10.1109/TGRS.2025.3600105
398. Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection Y. Zhou et al. https://doi.org/10.1109/TGRS.2022.3196040
399. Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review D. Peng et al. https://doi.org/10.1111/phor.70021
400. Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network for Remote Sensing Image Change Detection L. Li et al. https://doi.org/10.3390/rs18162763
401. Remote Sensing Image Change Detection Network Based on Twin High-Resolution Representation X. Wu et al. https://doi.org/10.1155/2023/4200153
402. Detail Enhanced Change Detection in VHR Images Using a Self-Supervised Multiscale Hybrid Network D. Zheng et al. https://doi.org/10.1109/JSTARS.2023.3348630
403. LCDNet: Lightweight Change Detection Network With Dual-Attention Guidance and Multiscale Feature Fusion for Remote-Sensing Images J. Li et al. https://doi.org/10.1109/LGRS.2023.3337877
404. Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges W. Shi et al. https://doi.org/10.3390/rs12101688
405. HFNet: Semantic and Differential Heterogenous Fusion Network for Remote Sensing Image Change Detection Y. Han et al. https://doi.org/10.1007/s41651-024-00202-3
406. Change Detection Mamba With Boundary-Specific Supervision G. Wang et al. https://doi.org/10.1109/TCSVT.2025.3629814
407. Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping S. Shi et al. https://doi.org/10.1080/17538947.2023.2246445
408. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ D. Peng et al. https://doi.org/10.3390/rs11111382
409. MANet: An Efficient Multidimensional Attention-Aggregated Network for Remote Sensing Image Change Detection K. Jiang et al. https://doi.org/10.1109/TGRS.2023.3328334
410. CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection L. Wan et al. https://doi.org/10.1109/JSTARS.2023.3327340
411. MTP: Advancing Remote Sensing Foundation Model via Multitask Pretraining D. Wang et al. https://doi.org/10.1109/JSTARS.2024.3408154
412. A Novel Remote Sensing Image Change Detection Approach Based on Multilevel State Space Model Z. Zhang et al. https://doi.org/10.1109/TGRS.2024.3501303
413. ForkNet: Strong Semantic Feature Representation and Subregion Supervision for Accurate Remote Sensing Change Detection H. He et al. https://doi.org/10.1109/JSTARS.2022.3152775
414. RISNet: Robust Ill-Posed Solver for Remote Sensing Image Change Detection Y. Chong et al. https://doi.org/10.1109/JSTARS.2025.3541260
415. SChanger: Change Detection From a Semantic Change and Spatial Consistency Perspective Z. Zhou et al. https://doi.org/10.1109/JSTARS.2025.3555849
416. Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images Y. Zuo et al. https://doi.org/10.1109/TGRS.2024.3379223
417. CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding D. Muhtar et al. https://doi.org/10.1109/TGRS.2023.3268232
418. Edge-Guided Parallel Network for VHR Remote Sensing Image Change Detection Y. Zhu et al. https://doi.org/10.1109/JSTARS.2023.3306274
419. MDENet: Multidomain Differential Excavating Network for Remote Sensing Image Change Detection J. Liu et al. https://doi.org/10.1109/TGRS.2024.3413677
420. SPRMamba: A Mamba-Based Saliency Proportion Reconciliatory Network With Squeezed Windows for Remote Sensing Change Detection S. Zhou et al. https://doi.org/10.1109/TGRS.2025.3587704
421. Using Adversarial Network for Multiple Change Detection in Bitemporal Remote Sensing Imagery W. Zhao et al. https://doi.org/10.1109/LGRS.2020.3035780
422. MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images X. Yang et al. https://doi.org/10.3390/rs13224528
423. HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation C. Pang et al. https://doi.org/10.1109/TGRS.2024.3367778
424. A Full-Scale Feature Fusion Siamese Network for Remote Sensing Change Detection H. Zhou et al. https://doi.org/10.3390/electronics12010035
425. Multiscale Fusion CNN-Transformer Network for High-Resolution Remote Sensing Image Change Detection M. Jiang et al. https://doi.org/10.1109/JSTARS.2024.3361507
426. Multi-class classification of parcel-level morphological urban change using a Siamese network: Evidence from Portland, Oregon Y. Yang et al. https://doi.org/10.1177/23998083261476101
427. Spatially and Semantically Enhanced Siamese Network for Semantic Change Detection in High-Resolution Remote Sensing Images M. Zhao et al. https://doi.org/10.1109/JSTARS.2022.3159528
428. ST-Mamba: Spatio-Temporal Synergistic Model for Remote Sensing Change Detection J. Zhao et al. https://doi.org/10.1109/TGRS.2025.3579617
429. Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment M. Areti & Z. Hasnain https://doi.org/10.1061/(ASCE)CP.1943-5487.0001036
430. Prototype Discriminative Learning for Semi-Supervised Change Detection in Remote Sensing Images Z. You et al. https://doi.org/10.1109/TGRS.2024.3491111
431. CFMNet: A Change-Similarity Feature Mixing Network for Remote Sensing Change Detection C. Zhang et al. https://doi.org/10.1109/TGRS.2026.3689765
432. Mamba-MSCCA-Net: Efficient change detection for remote sensing images Z. Song et al. https://doi.org/10.1016/j.displa.2025.103097