이전|다음

SAR 이미지 기반 양시점 변화 감지 연구 개요

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：202311070410@std.uestc.edu.cn
  • Introduction：刘玉婷，研究方向为土地利用变化检测。E-mail： 202311070410@std.uestc.edu.cn
LIU Yuting1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

    The Innovation Center for Technological Engineering of Ecological Protection, Restoration, and Comprehensive Improvement of Land and Space in Southwest China, Ministry of Natural Resources, Chengdu 610045, China

  • Email：lishihua@uestc.edu.cn
  • Introduction：李世华，研究方向为农业遥感。E-mail： lishihua@uestc.edu.cn
LI Shihua12*,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

HE Ze1,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

LIU Kaitong1

추상적인

원격 탐지 변화 감지는 도시 계획, 재해 평가, 자원 관리 등 다양한 분야에서 널리 사용되는 중요한 모니터링 기술입니다. 합성 개구 레이더(SAR)는全天시 및全天후 이미징 기능을 갖추고 있어 기상 및 조명 조건에 따른 광학 변화 감지의 문제를 극복합니다. SAR의 마이크로파 투과 능력, 편파 특성 및 간섭 영상 메커니즘은 지표면 아래 및 가려진 영역의 변화, 물체 구조 및 물리적 특성 변화, 그리고 미세 변화를 감지하는 데 독특한 이점을 제공합니다. 현재 대부분의 원격 탐지 양시점 변화 감지 개요는 주로 광학 원격 탐지 이미지에 집중되어 있으며, SAR 이미지 기반 변화 감지에 대한 체계적이고 목표 지향적인 정리가 부족합니다. 또한 심층 학습 및 다중 소스 융합 기술의 발전과 함께, SAR 이미지 기반 동형 및 이형 변화 감지가 첨단 연구 영역이 되었습니다. 따라서 본 논문은 국내외 수백 편의 고전 및 최신 문헌과 SAR 위성 영상 원리를 바탕으로, 먼저 다양한 촬영 조건에서의 변화 감지 대표 응용 사례를 논의했습니다. 그 후 데이터 수집, 이미지 전처리, 변화 인식 및 후처리 단계를 포함하는 양시점 변화 감지의 일반적인 프로세스를 구축했습니다. 이를 바탕으로 주류 변화 감지 데이터 및 방법을 체계적으로 정리했습니다. 데이터 부분은 SAR 기반의 동형 및 이형 변화 감지 오픈소스 데이터셋을 포함하며, 방법 부분은 전통적인 방법과 심층 학습 기반의 동형 및 이형 변화 감지 연구를 포함합니다. 특히 관련 데이터셋 및 모델 코드 링크를 정리하여 후속 연구에 중요한 참고 자료를 제공합니다. 마지막으로 본 논문은 데이터, 알고리즘 및 응용 3가지 측면에서 현 분야가 직면한 주요 과제들-데이터 품질 및 양, 모델 특성 표현력 및 계산 복잡도, 변화 감지 응용 범위 등을 요약했습니다. 이러한 과제들을 고려하여 미래 다중 모달 융합, 지능형 및 경량 모델 설계, 다중 변화 감지 발전 방향을 전망했습니다.

키워드

원격 탐지 양시점 변화 감지;SAR 이미지;전통적 방법;심층 학습;동형 및 이형

References

  1. 1.
    Alberga V. 2009. Similarity measures of remotely sensed multi-sensor images for change detection applications. Remote Sensing, 1(3): 122-143
  2. 2.
    Alves D I, Palm B G, Hellsten H, Vu V T, Pettersson M I, Machado R, Uchôa-Filho B F and Dammert P. 2020. Wavelength-resolution SAR change detection using Bayes’ theorem. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5560-5568
  3. 3.
    Ayhan B and Kwan C. 2019. A new approach to change detection using heterogeneous images//Proceedings of the 2019 10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference. New York: IEEE: 192-197
  4. 4.
    Baronti S, Carla R, Sigismondi S and Alparone L. 1994. Principal component analysis for change detection on polarimetric multitemporal SAR data//Proceedings of the 1994 IEEE International Geoscience and Remote Sensing Symposium. Pasadena: IEEE: 2152-2154
  5. 5.
    Barreto T L M, Rosa R A S, Wimmer C, Nogueira J B, Almeida J and Cappabianco F A M. 2016. Deforestation change detection using high-resolution multi-temporal X-Band SAR images and supervised learning classification//Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing: IEEE: 5201-5204
  6. 6.
    Bazi Y, Bruzzone L and Melgani F. 2005. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4): 874-887
  7. 7.
    Bazi Y, Bruzzone L and Melgani F. 2006. Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 3(3): 349-353
  8. 8.
    Bazi Y, Bruzzone L and Melgani F. 2007. Image thresholding based on the EM algorithm and the generalized Gaussian distribution. Pattern Recognition, 40(2): 619-634
  9. 9.
    Bazi Y, Melgani F and Al-Sharari H D. 2010. Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Transactions on Geoscience and Remote Sensing, 48(8): 3178-3187
  10. 10.
    Bonafilia D, Tellman B, Anderson T and Issenberg E. 2020. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE: 835-845
  11. 11.
    Bovolo F and Bruzzone L. 2005. A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(12): 2963-2972
  12. 12.
    Bovolo F and Bruzzone L. 2007. A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 218-236
  13. 13.
    Bovolo F, Marin C and Bruzzone L. 2013. A hierarchical approach to change detection in very high resolution SAR images for surveillance applications. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2042-2054
  14. 14.
    Brisco B, Schmitt A, Murnaghan K, Kaya S and Roth A. 2013. SAR polarimetric change detection for flooded vegetation. International Journal of Digital Earth, 6(2): 103-114
  15. 15.
    Brunner D, Lemoine G and Bruzzone L. 2010. Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2403-2420
  16. 16.
    Cable J W, Kovacs J M, Shang J L and Jiao X F. 2014. Multi-temporal polarimetric RADARSAT-2 for land cover monitoring in Northeastern Ontario, Canada. Remote Sensing, 6(3): 2372-2392
  17. 17.
    Cao W X and Martinis S. 2015. SAR-based change detection using hypothesis testing and Markov random field modelling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3: 783-790
  18. 18.
    Cartus O, Siqueira P and Kellndorfer J. 2018. An error model for mapping forest cover and forest cover change using L-band SAR. IEEE Geoscience and Remote Sensing Letters, 15(1): 107-111
  19. 19.
    Chan T F and Vese L A. 2001. Active contours without edges. IEEE Transactions on Image Processing, 10(2): 266-277
  20. 20.
    Chen G, Zhao Y N and Mouthaan K. 2025. Joint spatial-frequency scattering network for unsupervised SAR image change detection. IEEE Geoscience and Remote Sensing Letters, 22: 4004505
  21. 21.
    Chen H R X, Yokoya N, Wu C and Du B. 2022. Unsupervised multimodal change detection based on structural relationship graph representation learning. IEEE Transactions on Geoscience and Remote Sensing, 60: 5635318
  22. 22.
    Chen Y X and Bruzzone L. 2022. A self-supervised approach to pixel-level change detection in bi-temporal RS images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4413911
  23. 23.
    Cheng G L, Huang Y M, Li X T, Lyu S C, Xu Z Y, Zhao H B, Zhao Q and Xiang S M. 2024. Change detection methods for remote sensing in the last decade: a comprehensive review. Remote Sensing, 16(13): 2355
  24. 24.
    Cheng Y Q, Li H C, Celik T and Zhang F. 2013. FRFT-based improved algorithm of unsupervised change detection in SAR images via PCA and K-means clustering//Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne: IEEE: 1952-1955
  25. 25.
    Conradsen K, Nielsen A A, Schou J and Skriver H. 2003. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(1): 4-19
  26. 26.
    Cui B, Peng Y, Zhang Y H, Yin H J, Fang H, Guo S C and Du P J. 2024. Enhanced edge information and prototype constrained clustering for SAR change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 5206116
  27. 27.
    Cui B, Zhang Y H, Yan L, Wei J J and Huang Q L. 2019. A SAR change detection method based on the consistency of single-pixel difference and neighbourhood difference. Remote Sensing Letters, 10(5): 488-495
  28. 28.
    Dantas C F, Gaetano R and Ienco D. 2024. Semi-supervised heterogeneous domain adaptation via disentanglement and pseudo-labelling//Proceedings of the 2024 European Conference on Machine Learning and Knowledge Discovery in Databases: Research Track. Vilnius: Springer: 440-456
  29. 29.
    Dong H H, Jiao L C, Ma W P, Liu F, Liu X, Li L L and Yang S Y. 2022. Deep shearlet network for change detection in SAR images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5241115
  30. 30.
    Dong H H, Ma W P, Jiao L C, Liu F, Liu X and Zhu H. 2024. Contrastive learning with context-augmented transformer for change detection in SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 17710–17724
  31. 31.
    Ebel P, Saha S and Zhu X X. 2021. Fusing multi-modal data for supervised change detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2021: 243-249
  32. 32.
    Gao Y H, Gao F, Dong J Y, Du Q and Li H C. 2021. Synthetic aperture radar image change detection via Siamese adaptive fusion network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 10748-10760
  33. 33.
    Gong M G, Cao Y and Wu Q D. 2012. A neighborhood-based ratio approach for change detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 9(2): 307-311
  34. 34.
    Gong M G, Gao T Q, Zhang M Y, Li W, Wang Z B and Li D Z. 2023. An M-Nary SAR image change detection based on GAN architecture search. IEEE Transactions on Geoscience and Remote Sensing, 61: 4503718
  35. 35.
    Gong M G, Li Y, Jiao L C, Jia M and Su L Z. 2014a. SAR change detection based on intensity and texture changes. ISPRS Journal of Photogrammetry and Remote Sensing, 93: 123-135
  36. 36.
    Gong M G, Su L Z, Jia M and Chen W S. 2014b. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Transactions on Fuzzy Systems, 22(1): 98-109
  37. 37.
    Gong M G, Zhang P Z, Su L Z and Liu J. 2016. Coupled dictionary learning for change detection from multisource data. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7077-7091
  38. 38.
    Hafner S, Ban Y F and Nascetti A. 2023. Semi-supervised urban change detection using multi-modal Sentinel-1 SAR and Sentinel-2 MSI data. Remote Sensing, 15(21): 5135
  39. 39.
    Hafner S, Nascetti A, Azizpour H and Ban Y F. 2022. Sentinel-1 and Sentinel-2 data fusion for urban change detection using a dual stream U-Net. IEEE Geoscience and Remote Sensing Letters, 19: 4019805
  40. 40.
    He X N, Zhang S C, Xue B W, Zhao T and Wu T. 2023. Cross-modal change detection flood extraction based on convolutional neural network. International Journal of Applied Earth Observation and Geoinformation, 117: 103197
  41. 41.
    Hou X, Bai Y P, Xie Y F, Ge H B, Li Y, Shang C J and Shen Q. 2023. Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images. Knowledge-Based Systems, 264: 110281
  42. 42.
    Inglada J and Mercier G. 2007. A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis. IEEE Transactions on Geoscience and Remote Sensing, 45(5): 1432-1445
  43. 43.
    Irwin K, Braun A, Fotopoulos G, Roth A and Wessel B. 2018. Assessing single-polarization and dual-polarization TerraSAR-X data for surface water monitoring. Remote Sensing, 10(6): 949
  44. 44.
    Ji G Y, Dong Y W, Bu Y C, Li Y L, Zhou L J and Liang X D. 2018. Multi-band SAR coherent change detection method based on coherent representation differences of targets. Journal of Radars, 7(4): 455-464
  45. 45.
    Jiang F L, Huang B, Wu H S, Feng D, Zhou Y, Zhang M Y, Gong M G, Zhao W and Guan Z Y. 2025. Change masked modality alignment network for multimodal change detection. IEEE Transactions on Geoscience and Remote Sensing, 63: 4400316
  46. 46.
    Jiang H W, Peng M, Zhong Y J, Xie H F, Hao Z M, Lin J M, Ma X L and Hu X Y. 2022a. A survey on deep learning-based change detection from high-resolution remote sensing images. Remote Sensing, 14(7): 1552
  47. 47.
    Jiang X, Li G, Liu Y, Zhang X P and He Y. 2020. Change detection in heterogeneous optical and SAR remote sensing images via deep homogeneous feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1551-1566
  48. 48.
    Jiang X, Li G, Zhang X P and He Y. 2022b. A semisupervised siamese network for efficient change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4700718
  49. 49.
    Jing W, Bai H C, Song B B, Ni W P, Wu J Z and Wang Q. 2025. HeteCD: feature consistency alignment and difference mining for heterogeneous remote sensing image change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 223: 317-327
  50. 50.
    Khoshnevis S A and Ghorshi S. 2020. A tutorial on tomographic synthetic aperture radar methods. SN Applied Sciences, 2(9): 1504
  51. 51.
    Kittler J and Illingworth J. 1986. Minimum error thresholding. Pattern Recognition, 19(1): 41-47
  52. 52.
    Koeniguer E, Weissgerber F and Janez F. 2014. Urban change detection by comparing SAR images at different resolutions and polarimetric modes//Proceedings of the 2014 10th European Conference on Synthetic Aperture Radar. Berlin: VDE: 1-4
  53. 53.
    Lei T, Jia X H, Zhang Y N, He L F, Meng H Y and Nandi A K. 2018. Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Systems, 26(5): 3027-3041
  54. 54.
    Li H C, Yang G, Yang W, Du Q and Emery W J. 2020. Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 160: 167-179
  55. 55.
    Li H L, Zou B, Zhang L M and Qin J. 2024a. CausalCD: a causal graph contrastive learning framework for self-supervised SAR image change detection. IEEE Transactions on Geoscience and Remote Sensing, 62: 5217016
  56. 56.
    Li H L, Zou B, Zhang L M and Qin J. 2024b. Semi-supervised SAR image change detection via structure-optimized complex-valued graph contrastive learning. IEEE Geoscience and Remote Sensing Letters, 21: 4012905
  57. 57.
    Li J J, Jiao L C, Zhang X R and Yang D D. 2010. Change detection for SAR images based on joint-classification of bi-temporal images. Journal of Infrared and Millimeter Waves, 28(6): 466-471
  58. 58.
    Liu L Y, Jia Z H, Yang J and Kasabov N K. 2019. SAR image change detection based on mathematical morphology and the K-means clustering algorithm. IEEE Access, 7: 43970-43978
  59. 59.
    Liu T F, Xu J J, Lei T, Wang Y B, Du X G, Zhang W C, Lv Z Y and Gong M G. 2025. AEKAN: exploring superpixel-based autoencoder Kolmogorov-Arnold network for unsupervised multimodal change detection. IEEE Transactions on Geoscience and Remote Sensing, 63: 5601114
  60. 60.
    Liu Z, Lin Y T, Cao Y, Hu H, Wei Y X, Zhang Z, Lin S and Guo B N. 2021a. Swin Transformer: hierarchical vision transformer using shifted windows//Proceedings of the 2011 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE: 9992-10002
  61. 61.
    Liu Z G, Li G, Mercier G, He Y and Pan Q. 2018. Change detection in heterogenous remote sensing images via homogeneous pixel transformation. IEEE Transactions on Image Processing, 27(4): 1822-1834
  62. 62.
    Liu Z G, Qiu G H, Mercier G and Pan Q. 2021b. A transfer classification method for heterogeneous data based on evidence theory. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8): 5129-5141
  63. 63.
    Liu Z G, Zhang L, Li G and He Y. 2017. Change detection in heterogeneous remote sensing images based on the fusion of pixel transformation//Proceedings of the 2017 20th IEEE International Conference on Information Fusion. Xi'an: IEEE: 1-6
  64. 64.
    Lu T J, Zhong X and Zhong L. 2024. mSwinUNet: a multi-modal U-shaped swin transformer for supervised change detection. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, 46(2): 4243-4252
  65. 65.
    Lu Z and Meyer D J. 2002. Study of high SAR backscattering caused by an increase of soil moisture over a sparsely vegetated area: implications for characteristics of backscattering. International Journal of Remote Sensing, 23(6): 1063-1074
  66. 66.
    Luppino L T, Bianchi F M, Moser G and Anfinsen S N. 2019. Unsupervised image regression for heterogeneous change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9960-9975
  67. 67.
    Luppino L T, Kampffmeyer M, Bianchi F M, Moser G, Serpico S B, Jenssen R and Anfinsen S N. 2022. Deep image translation with an affinity-based change prior for unsupervised multimodal change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 4700422
  68. 68.
    Marin C, Bovolo F and Bruzzone L. 2015. Building change detection in multitemporal very high resolution SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2664-2682
  69. 69.
    Meng W H, Wang L J, Du A Y and Li Y M. 2020. SAR image change detection based on data optimization and self-supervised learning. IEEE Access, 8: 217290-217305
  70. 70.
    Mercier G, Moser G and Serpico S B. 2008. Conditional copulas for change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 46(5): 1428-1441
  71. 71.
    Moghimi A, Khazai S and Mohammadzadeh A. 2017. An improved fast level set method initialized with a combination of K-means clustering and Otsu thresholding for unsupervised change detection from SAR images. Arabian Journal of Geosciences, 10(13): 293
  72. 72.
    Monti-Guarnieri A V, Brovelli M A, Manzoni M, d’Alessandro M M, Molinari M E and Oxoli D. 2018. Coherent change detection for multipass SAR. IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6811-6822
  73. 73.
    Moser G, Serpico S B and Vernazza G. 2013. Multiresolution SAR data fusion for unsupervised change detection//Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne: IEEE: 1234-1237
  74. 74.
    Mubea K and Menz G. 2012. Monitoring land-use change in Nakuru (Kenya) using multi-sensor satellite data. Advances in Remote Sensing, 1(3): 74-84
  75. 75.
    Novak L M. 2005. Coherent change detection for multi-polarization SAR//Proceedings of the 2005 IEEE Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers. Pacific Grove: IEEE: 568-573
  76. 76.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  77. 77.
    Peng Y, Cui B, Yin H J, Zhang Y H and Du P J. 2022. Automatic SAR change detection based on visual saliency and multi-hierarchical fuzzy clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 7755-7769
  78. 78.
    Pirrone D, Bovolo F and Bruzzone L. 2016. A novel framework for change detection in bi-temporal polarimetric SAR images//Proceedings of SPIE 10004, Image and Signal Processing for Remote Sensing XXII. Edinburgh: SPIE: 100040Z
  79. 79.
    Pun T. 1980. A new method for grey-level picture thresholding using the entropy of the histogram. Signal Processing, 2(3): 223-237
  80. 80.
    Qi Z X, Yeh A G O, Li X and Zhang X H. 2015. A three-component method for timely detection of land cover changes using polarimetric SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 107: 3-21
  81. 81.
    Qu X F, Gao F, Dong J Y, Du Q and Li H C. 2022. Change detection in synthetic aperture radar images using a dual-domain network. IEEE Geoscience and Remote Sensing Letters, 19: 4013405
  82. 82.
    Ratha D, De S, Celik T and Bhattacharya A. 2017. Change detection in polarimetric SAR images using a geodesic distance between scattering mechanisms. IEEE Geoscience and Remote Sensing Letters, 14(7): 1066-1070
  83. 83.
    Rignot E J M and van Zyl J J. 1993. Change detection techniques for ERS-1 SAR data. IEEE Transactions on Geoscience and Remote Sensing, 31(4): 896-906
  84. 84.
    Rosa R A S, Fernandes D, Barreto T L M, Wimmer C and Nogueira J B. 2017. Deforestation detection in Amazon rainforest with multitemporal X-band and P-band SAR images using cross-coherences and superpixels//Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE: 3015-3018
  85. 85.
    Saha S, Ebel P and Zhu X X. 2022. Self-supervised multisensor change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 4405710
  86. 86.
    Saleh T, Holail S, Zahran M, Xiao X W and Xia G S. 2024a. LiST-Net: enhanced flood mapping with lightweight SAR transformer network and dimension-wise attention. IEEE Transactions on Geoscience and Remote Sensing, 62: 5211817
  87. 87.
    Saleh T, Weng X X, Holail S, Hao C and Xia G S. 2024b. DAM-Net: flood detection from SAR imagery using differential attention metric-based vision transformers. ISPRS Journal of Photogrammetry and Remote Sensing, 212: 440-453
  88. 88.
    Shafique A, Cao G, Khan Z, Asad M and Aslam M. 2022. Deep learning-based change detection in remote sensing images: a review. Remote Sensing, 14(4): 871
  89. 89.
    Shen Y, Yao S, Qiang Z K and Pei G X. 2025. SD-Mamba: a lightweight synthetic-decompression network for cross-modal flood change detection. International Journal of Applied Earth Observation and Geoinformation, 136: 104409
  90. 90.
    Shi W Z, Zhang M, Zhang R, Chen S X and Zhan Z. 2020. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sensing, 12(10): 1688
  91. 91.
    Shi X, Xing M D, Zhang J S, Liu H T and Wang H X. 2024. SAR multi-satellite collaborative complex area observation planning based on improved genetic algorithm. National Remote Sensing Bulletin, 28(7): 1822-1834
  92. 92.
    Shuai Y M, Xu X, Sun H and Xu G. 2006. Change detection based on region likelihood ratio in multitemporal SAR images//Proceedings of the 2006 8th International Conference on Signal Processing. Guilin: IEEE: 1 [DOI: 10.1109/ICOSP.2006.345675]
  93. 93.
    Solarna D, Moser G and Serpico S B. 2019. Multiresolution and multimodality SAR data fusion based on markov and conditional random fields for unsupervised change detection//Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 29-32
  94. 94.
    Sumaiya M N and Shantha Selva Kumari R. 2017. Gabor filter based change detection in SAR images by KI thresholding. Optik, 130: 114-122
  95. 95.
    Sun Y L, Lei L, Guan D D, Kuang G Y, Li Z and Liu L. 2025a. Locality preservation for unsupervised multimodal change detection in remote sensing imagery. IEEE Transactions on Neural Networks and Learning Systems, 36(4): 6955-6969
  96. 96.
    Sun Y L, Lei L, Li X, Sun H and Kuang G Y. 2021. Nonlocal patch similarity based heterogeneous remote sensing change detection. Pattern Recognition, 109: 107598
  97. 97.
    Sun Y L, Lei L, Liu L and Kuang G Y. 2023. Structural regression fusion for unsupervised multimodal change detection. IEEE Transactions on Geoscience and Remote Sensing, 61: 4504018
  98. 98.
    Sun Y L, Li M, Lei L, Li Z and Kuang G Y. 2025b. Signed graph-based image transformation for heterogeneous change detection. IEEE Transactions on Geoscience and Remote Sensing, 63: 4403618
  99. 99.
    Tang Y Q, Lin Z F, Han T, Yang X, Zou B and Feng H H. 2024. Optical and SAR image change detection based on a symmetric network. National Remote Sensing Bulletin, 28(6): 1560-1575
  100. 100.
    Tiampo K F, Huang L C, Simmons C, Woods C and Glasscoe M T. 2022. Detection of flood extent using Sentinel-1A/B synthetic aperture radar: an application for Hurricane Harvey, Houston, TX. Remote Sensing, 14(9): 2261
  101. 101.
    Tirandaz Z and Akbarizadeh G. 2016. A two-phase algorithm based on kurtosis curvelet energy and unsupervised spectral regression for segmentation of SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(3): 1244-1264
  102. 102.
    Wahl D E, Yocky D A, Jakowatz C V and Simonson K M. 2016. A new maximum-likelihood change estimator for two-pass SAR coherent change detection. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2460-2469
  103. 103.
    Wan L, Xiang Y M and You H J. 2019a. An object-based hierarchical compound classification method for change detection in heterogeneous optical and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9941-9959
  104. 104.
    Wan L, Xiang Y M and You H J. 2019b. A post-classification comparison method for SAR and optical images change detection. IEEE Geoscience and Remote Sensing Letters, 16(7): 1026-1030
  105. 105.
    Wan L, Zhang T and You H J. 2018. Multi-sensor remote sensing image change detection based on sorted histograms. International Journal of Remote Sensing, 39(11): 3753-3775
  106. 106.
    Wang J, Wang Y H, Chen B and Liu H W. 2021. LCS-EnsemNet: a semisupervised deep neural network for SAR image change detection with dual feature extraction and label-consistent self-ensemble. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 11903-11925
  107. 107.
    Wang J, Wang Y H and Liu H W. 2022a. Hybrid variability aware network (HVANet): a self-supervised deep framework for label-free SAR image change detection. Remote Sensing, 14(3): 734
  108. 108.
    Wang J J, Gao F, Dong J Y, Zhang S and Du Q. 2022b. Change detection from synthetic aperture radar images via graph-based knowledge supplement network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 1823-1836
  109. 109.
    Wang R F, Wang L, Wei X H, Chen J W and Jiao L C. 2022c. Dynamic graph-level neural network for SAR image change detection. IEEE Geoscience and Remote Sensing Letters, 19: 4501005
  110. 110.
    Wang S N, Liu Y, Li L L, Zhang Z H and Wang D. 2023. A summary of SAR image change detection. Laser Journal, 44(1): 8-15
  111. 111.
    Wang T and Liao M S. 2018. Coseismic displacement derived from Sentinel-1 data: latest techniques and case studies. Journal of Remote Sensing (in Chinese), 22(S1): 120-127
  112. 112.
    Wang X H, Cheng W, Feng Y N and Song R X. 2023. TSCNet: topological structure coupling network for change detection of heterogeneous remote sensing images. Remote Sensing, 15(3): 621
  113. 113.
    Watanabe M, Koyama C, Hayashi M, Nagatani I, Tadono T and Shimada M. 2018. Semi-automatic deforestation detection algorithm with PALSAR-2/ScanSAR HH/HV polarizations//Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 4177-4180
  114. 114.
    Watanabe M, Koyama C N, Hayashi M, Nagatani I, Tadono T and Shimada M. 2021. Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions. Remote Sensing of Environment, 265: 112643
  115. 115.
    Wu H R, Geng J and Jiang W. 2024a. Multidomain constrained translation network for change detection in heterogeneous remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 62: 5616916
  116. 116.
    Wu J S, Yang S W, Li Y K, Fu Y K, Shi Z and Zheng Y. 2024b. Self-supervised change detection of heterogeneous images based on difference algorithms. European Journal of Remote Sensing, 57(1): 2372854
  117. 117.
    Wu J Z, Li B, Qin Y, Ni W P, Zhang H, Fu R G and Sun Y L. 2021. A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 105: 102615
  118. 118.
    Xing M D, Lin H, Chen J L, Sun G C and Yan B B. 2019. A review of imaging algorithms in multi-platform-borne synthetic aperture radar. Journal of Radars, 8(6): 732-757
  119. 119.
    Yadav R, Nascetti A and Ban Y F. 2022. Attentive dual stream siamese U-Net for flood detection on multi-temporal Sentinel-1 data//Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur: IEEE: 5222-5225
  120. 120.
    Yan T Y, Wan Z F, Zhang P P, Cheng G and Lu H C. 2023. TransY-Net: learning fully transformer networks for change detection of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61: 4410012
  121. 121.
    Yang M J, Jiao L C, Liu F, Hou B, Yang S Y, Zhang Y K and Wang J L. 2023a. Multicue contrastive self-supervised learning for change detection in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 61: 5221014
  122. 122.
    Yang Z F, Wu Y, Li M, Hu X and Li Z K. 2023b. Unsupervised change detection in PolSAR images using siamese encoder–decoder framework based on graph-context attention network. International Journal of Applied Earth Observation and Geoinformation, 124: 103511
  123. 123.
    Zhan T, Dang Q L and Zhu Y Y. 2024. Neighborhood difference-based self-supervised network for detecting small changes from synthetic aperture radar images. IEEE Geoscience and Remote Sensing Letters, 21: 4012305
  124. 124.
    Zhang C X, Feng Y K, Hu L, Tapete D, Pan L, Liang Z H, Cigna F and Yue P. 2022. A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 109: 102769
  125. 125.
    Zhang D J, Hong Y T, Qiu X J, Dong L, Yan D Q and Peng C B. 2024. C²F²: cross-task cross-domain feature fusion for semi-supervised change detection. IEEE Geoscience and Remote Sensing Letters, 21: 6016405
  126. 126.
    Zhang E L, Zong H, Li X Y, Feng M C and Ren J C. 2025. ICSF: integrating inter-modal and cross-modal learning framework for self-supervised heterogeneous change detection. IEEE Transactions on Geoscience and Remote Sensing, 63: 5501516
  127. 127.
    Zhang H, Qi Z X, Li X, Chen Y M, Wang X W and He Y Q. 2021. An urban flooding index for unsupervised inundated urban area detection using Sentinel-1 polarimetric SAR images. Remote Sensing, 13(22): 4511
  128. 128.
    Zhang Y, Wang S G, Wang C, Li J X and Zhang H. 2018. SAR image change detection using saliency extraction and shearlet transform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12): 4701-4710
  129. 129.
    Zhang Y H, Zhang X F, Fu J and Jin S S. 2014. A SAR change detection method based on polarimetric distance measure. Acta Geodaetica et Cartographica Sinica, 43(2): 143-150
  130. 130.
    Zheng N R, Yang Z A, Shi X Z, Yang H, Sun Y and Wang F. 2024. Airborne multidimensional SAR land cover dataset and fusion classification method. National Remote Sensing Bulletin, 28(9): 2209-2222
  131. 131.
    Zheng Y G, Zhang X R, Hou B and Liu G C. 2014. Using combined difference image and k-means clustering for SAR image change detection. IEEE Geoscience and Remote Sensing Letters, 11(3): 691-695
  132. 132.
    Zhu L Y, Sun W B, Fan D Q, Xing H Q and Liu X Q. 2024a. Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images. Pattern Recognition, 149: 110237
  133. 133.
    Zhu Q Q, Guo X, Li Z Q and Li D R. 2024b. A review of multi-class change detection for satellite remote sensing imagery. Geo-spatial Information Science, 27(1): 1-15
  134. 134.
    Zhuang H F, Fan H D, Deng K Z and Yu Y. 2018. An improved neighborhood-based ratio approach for change detection in SAR images. European Journal of Remote Sensing, 51(1): 723-738
  135. 135.
    Zou C Y, Zhao L L, Sun P L, Fu B X and Su X. 2022. New building detection using sar images with different resolutions//Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur: IEEE: 3680-3683

더 읽기

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website