Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (20): 30-48.DOI: 10.3778/j.issn.1002-8331.2404-0392
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SUN Jianming, ZHAO Mengxin, HAO Xuyao
Online:
2024-10-15
Published:
2024-10-15
孙剑明,赵梦鑫,郝旭耀
SUN Jianming, ZHAO Mengxin, HAO Xuyao. Research Review of Remote Sensing Image Change Detection Methods[J]. Computer Engineering and Applications, 2024, 60(20): 30-48.
孙剑明, 赵梦鑫, 郝旭耀. 遥感图像变化检测方法研究综述[J]. 计算机工程与应用, 2024, 60(20): 30-48.
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[1] GONG M, YANG H, ZHANG P. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 129: 212-225. [2] 吴芳, 刘荣, 田维春, 等. 遥感变化检测技术及其应用综述[J]. 地理空间信息, 2007(4): 57-60. WU F, LIU R, TIAN W C, et al. Technology for remote sensing change detection and its application[J]. Geospatial Information, 2007(4): 57-60. [3] 张振龙, 曾志远, 李硕, 等. 遥感变化检测方法研究综述[J]. 遥感信息, 2005(5): 64-66. ZHANG Z L, ZENG Z Y, LI S, et al. A summary of change detection methods of remote sensing image[J]. Remote Sensing Information, 2005(5): 64-66. [4] 陈鑫镖. 遥感影像变化检测技术发展综述[J]. 测绘与空间地理信息, 2012, 35(9): 38-41. CHEN X B. A summary of change detection techniques of remote sensing imagery[J]. Surveying, Mapping and Spatial Geographic Information, 2012, 35(9): 38-41. [5] 任秋如, 杨文忠, 汪传建, 等. 遥感影像变化检测综述[J]. 计算机应用, 2021, 41(8): 2294-2305. REN Q R, YANG W Z, WANG C J, et al. Review of remote sensing image change detection[J]. Computer Applications, 2021, 41(8): 2294-2305. [6] WU C, ZHANG L, DU B. Kernel slow feature analysis for scene change detection[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(4): 2367-2384. [7] DAUDT R C, SAUX B L, BOULCH A, et al. Urban change detection for multispectral earth observation using convolutional neural networks[C]//Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018: 2115-2118. [8] JI S, WEI S, LU M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery dataset[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1): 574-586. [9] CHEN H, SHI Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10): 1662. [10] ZHANG C X, PENG Y, TAPETE D, et al. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166: 183-200. [11] LIU M X, CHAI Z, DENG H, et al. A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4297-4306. [12] LEBEDRV M A, VIZILTER Y V, VYGOLOV O V, et al. Change detection in remote sensing images using conditional adversarial networks[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, XLII-2: 565-571. [13] DAUDT R C, SAUX B L, BOULCH A, et al. Multitask learning for large-scale semantic change detection[J]. Computer Vision and Image Understanding, 2019, 187: 102783. [14] SHEN L, LU Y, CHEN H, et al. S2Looking: a satellite side-looking dataset for building change detection[J]. Remote Sensing, 2021, 13(24): 5094. [15] SHI Q, LIU M, LI S, et al. A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16. [16] 佟国峰, 李勇, 丁伟利, 等. 遥感影像变化检测算法综述[J]. 中国图象图形学报, 2015, 20(12): 1561-1571. TONG G F, LI Y, DING W L, et al. Review of remote sensing image change detection[J]. Chinese Journal of Image Graphics, 2015, 20(12): 1561-1571. [17] 眭海刚, 冯文卿, 李文卓, 等. 多时相遥感影像变化检测方法综述[J]. 武汉大学学报 (信息科学版), 2018, 43(12): 1885-1898. SUI H G, FENG W Q, LI W Z, et al. Review of change detection methods for multi-temporal remote sensing imagery[J]. Journal of Wuhan University (Information Science Edition), 2018, 43(12): 1885-1898. [18] 眭海刚, 王建勋, 华丽, 等. 遥感耕地监测现状与方法综述[J]. 广西科学, 2022, 29(1): 1-12. SUI H G, WANG J X, HUA L, et al. Review on the status and methods of remote sensing farmland monitoring[J]. Guangxi Science, 2022, 29(1): 1-12. [19] TUNG F. An assessment of tm imagery for land cover change detection[C]//Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 1989: 1998- 2001. [20] HERZOG J H. Change detection metrics and mixels (mixed picture elements) for computer analysis of low resolution remote sensing imagery[C]//Proceedings of the IEEE 1977 Region Six Conference Record, Portland, OR, USA, 1977: 17-22. [21] WHITE R G, OLIVER C J. Change detection in SAR imagery[C]//Proceedings of the IEEE Colloquium on Role of Image Processing in Defence and Military Electronics, London, UK, 1990: 1-3. [22] LI J C , QIAN S M, CHEN X. Object-oriented method of land cover change detection approach using high spatial resolution remote sensing data[C]//Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003: 3005-3007. [23] ASHOURLOO D, SHAHRABI H S, AZADBAKHT M, et al. A novel method for automatic potato mapping using time series of Sentinel-2 images[J]. Computers and Electronics in Agriculture, 2020, 175: 105583. [24] OPEDES H, MüCHER S, BAARTMAN J E M, et al. Land cover change detection and subsistence farming dynamics in the fringes of mount elgon national park, uganda from 1978—2020[J]. Remote Sensing, 2022, 14(10): 2423. [25] RIGNOT E, CHELLAPPA R. A Bayes classifier for change detection in synthetic aperture radar imagery[C]//Proceedings of the 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, USA, 1992: 25-28. [26] COULON M, TOURNERNET J Y T. Bayesian change detection for multi-temporal SAR images[C]//Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA, 2002: 1285-1288. [27] YANG Z G, ?QIN Q Q, ?ZHANG Q F. Change detection in high spatial resolution images based on support vector machine[C]//Proceedings of the 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA, 2006: 225-228. [28] WU Z, THENKABAIL P S, MUELLER R, et al. Seasonal cultivated and fallow cropland mapping using modis-based automated cropland classification algorithm[J]. Journal of Applied Remote Sensing, 2014, 8(1): 397-398. [29] FRATE F D, SCHIAVON G, SOLIMINI C. Application of neural networks algorithms to QuickBird imagery for classification and change detection of urban areas[C]//Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 2004: 1091-1094. [30] MOSER G, SERPICO S B. Unsupervised change detection by multichannel SAR data fusion[C]//Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007: 4854-4857. [31] KLENKE M, HOCHSCHILD V. Improving SAR intensity-based land cover classification results by the use of interferometric coherence information and GIS-analysis[C]//Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 1999: 2108-2110. [32] ZENG Y N, MA H Z, FEN Z D, et al. Detection of land use/cover change in the upper reaches of the yellow river by remote sensing and GIS: a case study[C]//Proceedings of the 2001 International Conferences on Info-Tech and Info-Net, Beijing, China, 2001: 152-158. [33] 李月臣, 陈晋, 宫鹏, 等. 基于NDVI时间序列数据的土地覆盖变化检测指标设计[J]. 应用基础与工程科学学报, 2005(3): 44-58. LI Y C, CHEN J, GONG P, et al. Study on land cover change detection method based on NDVI time series datasets change detection indexes design[J]. Journal of Applied Basic and Engineering Sciences, 2005(3): 44-58. [34] CHEN J, CHEN J, LIU H, et al. Detection of cropland change using multi-harmonic based phenological trajectory similarity[J]. Remote Sensing, 2018, 10(7): 1020. [35] LISTNER C, NIEMEYER I. Recent advances in object-based change detection[C]//Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 2011: 110-113. [36] LI X, SHU N, YANG J. et al. The land-use change detection method using object-based feature consistency analysis[C]//Proceedings of the 19th International Conference on Geoinformatics, Shanghai, China, 2011: 1-6. [37] LI Y H, DAVIS C H. Unsupervised change detection in high resolution satellite imagery from fusion of spectral and spatial information[C]//Proceedings of the 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 2008: 109-112. [38] WEN X P, YANG X F. A new change detection method for two remote sensing images based on spectral matching[C]//Proceedings of the 2009 International Conference on Industrial Mechatronics and Automation, Chengdu, China, 2009: 89-92. [39] TEJASWINI M, PRANUTHI P, RAVICHAND S, et al. Land cover change detection using convolution neural network[C]//Proceedings of the 3rd International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2019: 791-794. [40] XU J, ZHU Y, ZHONG R, et al. DeepCropMapping: a multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping[J]. Remote Sensing of Environment: an Interdisciplinary Journal, 2020, 247(1): 111946. [41] YANG B, QIN L, LIU J, et al. IRCNN: an irregular-time- distanced recurrent convolutional neural network for change detection in satellite time series[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5. [42] HENG L U , XIAO F U , CHAO L, et al. Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning[J]. Journal of Mountain Science, 2017, 14(4): 731-741. [43] ZHANG W, FAN H. Application of isolated forest algorithm in deep learning change detection of high resolution remote sensing image[C]//Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, Dalian, China, 2020: 753-756. [44] JIA X, HU Y, KHANDELWAL A, et al. Joint sparse auto-encoder: a semi-supervised spatio-temporal approach in mapping large-scale croplands[C]//Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017: 1173-1182. [45] VASWANI A, SHAZEER N M, PARMAR N, et al. Attention is all you need[C]//Proceedings of the Annual Conference on Neural Information Processing Systems, 2017: 5998- 6008. [46] BROMLEY J, GUYON I, LECUN Y, et al. Signature verification using a “siamese” time delay neural network[C]//Advances in Neural Information Processing Systems, 1994: 737-744. [47] 万幼川, 申邵洪, 张景雄. 基于概率统计模型的遥感影像变化检测[J]. 武汉大学学报 (信息科学版), 2008(7): 669-672. WAN Y C, S HEN S H, ZHANG J X. Change detection of multi-time remote sensing images based on statistics model[J]. Journal of Wuhan University (Information Science Edition), 2008(7): 669-672. [48] 李春干, 代华兵. 基于统计检验的面向对象高分辨率遥感图像森林变化检测[J]. 林业科学, 2017, 53(5): 74-81. LI C G, Dai H B. Statistical object-based method for forest change detection using high-resolution remote sensing images[J]. Forestry Science, 2017, 53(5): 74-81. [49] 张志强, 张新长, 辛秦川, 等. 结合像元级和目标级的高分辨率遥感影像建筑物变化检测[J]. 测绘学报, 2018, 47(1): 102-112. ZHANG Z Q, ZHANG X C, XIN Q C, et al. Combining the pixel-based and object-based methods for building change detection using high-resolution remote sensing images[J]. Journal of Surveying and Mapping, 2018, 47 (1): 102-112. [50] 郭擎, 朱丽娅, 李安, 等. 基于NDVI变化检测的滑坡遥感精细识别[J]. 遥感技术与应用, 2022, 37(1): 17-23. GUO Q, ZHU L Y, LI A, et al. Landslide identification method based on NDVI change detection[J]. Remote Sensing Technology and Application, 2022, 37(1): 17-23. [51] 闫利, 巩翼龙, 张毅, 等. 光流动态纹理在土地利用/覆盖变化检测研究中的应用[J]. 光谱学与光谱分析, 2014, 34(11): 3056-3061. YAN L, GONG Y L, ZHANG Y, et al. Application of optical flow dynamic texture in land use/cover change detection [J]. Spectroscopy and Spectral Analysis, 2014, 34(11): 3056-3061. [52] 李启发. 多特征与随机多图结合的建筑物变化检测方法[J]. 地理空间信息, 2023, 21(7): 19-22. LI Q F. Building change detection method based on multi-feature and random multi-graph fusion[J]. Geospatial Information, 2023, 21(7): 19-22. [53] 盛辉, 廖明生, 张路. 基于典型相关分析的变化检测中变化阈值的确定[J]. 遥感学报, 2004(5): 451-457. SHENG H, LIAO M S, ZHANG L. Determination of threshold in change detection based on canonical correlation analysis[J]. Journal of Remote Sensing, 2004(5): 451-457. [54] SHIMU S A, AKTAR M, AFJAL M I, et al. NDVI based change detection in sundarban mangrove forest using remote sensing data[C]//Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology, Khulna, Bangladesh, 2019: 1-5. [55] 潘建平, 徐永杰, 李明明, 等. 结合相关系数和特征分析的植被区域自动变化检测研发[J]. 自然资源遥感, 2022, 34(1): 67-75. PAN J P, XU Y J, LI M M, et al. Rearch and development of automatic detection technologies for changes in vegetation regions based on correlation coefficients and feature analysis[J]. Remote Sensing of Natural Resources, 2022, 34(1): 67-75. [56] 李亮, 舒宁, 王凯, 等. 融合多特征的遥感影像变化检测方法[J]. 测绘学报, 2014, 43(9): 945-953. LI L, SHU N, WANG K, et al. Change detection method for remote sensing images based on multi-features fusion[J]. Journal of Surveying and Mapping, 2014, 43(9): 945-953. [57] TURKER M, KOK E H. Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2013, 79: 106-121. [58] BERTOLUZZA M, NRUZZONE L, BOVOLO F. A novel framework for bi-temporal change detection in image time series[C]//Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA, 2017: 1087-1090. [59] 秦乐, 何鹏, 马玉忠, 等. 基于时空谱特征的遥感影像时间序列变化检测[J]. 自然资源遥感, 2022, 34(4): 105-112. QIN L, HE P, MA Y Z, et al. Change detection of satellite time series images based on spatial-temporal-spectral features[J]. Remote Sensing of Natural Resources, 2022, 34(4): 105-112. [60] 董岳, 王飞. KCCA与SVM算法支撑下的遥感影像变化检测[J]. 遥感信息, 2019, 34(1): 144-148. DONG Y, WANG F. Change detection of remote sensing imagery supported by KCCA and SVM algorithms[J]. Remote Sensing Information, 2019, 34(1): 144-148. [61] 叶沅鑫, 孙苗苗, 王蒙蒙, 等. 结合邻域信息和结构特征的遥感影像变化检测[J]. 测绘学报, 2021, 50(10): 1349-1357. YE Y X, SUN M M, WANG M M, et al. Change detection of remote sensing images by combining neighbourhood information and structural features[J]. Journal of Surveying and Mapping, 2021, 50(10): 1349-1357. [62] 窦世卿, 宋莹莹, 徐勇, 等. 基于随机森林的高分影像分类及土地利用变化检测[J]. 无线电工程, 2021, 51(9): 901-908. DOU S Q, SONG Y Y, XU Y, et al. High resolution image classification and land use change detection based on random forest[J]. Radio Engineering, 2021, 51(9): 901-908. [63] 陈雪, 马建文, 戴芹. 基于贝叶斯网络分类的遥感影像变化检测[J]. 遥感学报, 2005(6): 667-672. CHEN X, MA J W, DAI Q. Remote sensing change detection based on bayesian networks classifications[J]. Journal of Remote Sensing, 2005(6): 667-672. [64] NIE W, GOU P, LIU Y, et al. Semi supervised change detection method of remote sensing image[C]//Proceedings of the 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference, Beijing, China, 2022: 1013-1019. [65] ZHANG X L, WANG L, JIAO L C. An unsupervised change detection based on clustering combined with multiscale and region growing[C]//Proceedings of the 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping, Xiamen, China, 2011: 1-4. [66] 许石罗, 牛瑞卿, 武雪玲, 等. 基于主成分分析与粒子群优化的遥感影像变化检测[J]. 测绘科学, 2017, 42(4): 151-156. XU S L, NIU R Q, WU X L, et al. Change detection method of remote sensing image based on pca and pso algorithm[J]. Surveying and Mapping Science, 2017, 42(4): 151-156. [67] XU Q, PU Y, WANG W, et al. Multispectral remote sensing image change detection based on markovian fusion[C]//Proceedings of the 1st International Conference on Agro-Geoinformatics, Shanghai, China, 2012: 1-5. [68] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521: 436-444. [69] KHELIFI L, MIGNOTTE M. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis[J]. IEEE Access, 2020, 8: 126385-126400. [70] MAMDAL M, VIPPARTHI S K. An empirical review of deep learning frameworks for change detection: model design, experimental frameworks, challenges and research needs[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23: 6101-6122. [71] ZHU X X, TUIA D, MOU L C, et al. Deep learning in remote sensing: a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5: 8-36. [72] MA L, LIU Y, ZHANG X L, et al. Deep learning in remote sensing applications: a meta-analysis and review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 166-177. [73] RONNEBERGE O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[J]. arXiv:1505.04597, 2015. [74] YUAN J, WANG L, CHENG S. STransUNet: a siamese transunet-based remote sensing image change detection network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9241-9253. [75] 张翠军, 安冉, 马丽. 改进U-Net的遥感图像中建筑物变化检测[J]. 计算机工程与应用, 2021, 57(3): 239-246. ZHANG C J, AN R, MA L. Building change detection in remote sensing image based on improved U-Net[J]. Computer Engineering and Applications, 2021, 57(3): 239-246. [76] KRIZHEVSKY A, SUTSKEVER I, HINTON G. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012: 1097-1115. [77] 付青, 罗文浪, 吕敬祥. 基于AlexNet和支持向量机相结合的卫星遥感影像土地利用变化检测[J]. 激光与光电子学进展, 2020, 57(17): 282-290. FU Q, LUO W L, LYU J X. Land utilization change detection of satellite remote sensing image based on AlexNet and support vector machine[J]. Advances in Laser and Optoelectronics, 2020, 57(17): 282-290. [78] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409. 1556, 2014. [79] 黄梅, 杨文忠, 汪传建, 等. 基于SE-DRUnet的遥感影像耕地变化检测[J]. 东北师大学报 (自然科学版), 2022, 54(2): 61-67. HUANG M, YANG W Z, WANG C J, et al. Change detection for cultivated land in remote sensing images based on SE-DRUnet[J]. Journal of Northeast Normal University (Natural Science Edition), 2022, 54(2): 61-67. [80] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2016: 770-778. [81] CHEN H, QI Z, SHI Z. Remote sensing image change detection with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14. [82] 陈良轩, 于海洋, 李英成, 等. 一种融合注意力机制的建筑物变化检测模型[J]. 测绘科学, 2022, 47(4): 153-159. CHEN L X, YU H Y, LI Y C, et al. A model for detecting building changes incorporating attention mechanisms[J]. Surveying and Mapping Science, 2022, 47(4): 153-159. [83] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440. [84] DAUDT R C, SAUX B L, BOULCH A. Fully convolutional siamese networks for change detection[C]//Proceedings of the 25th IEEE International Conference on Image Processing, Athens, Greece, 2018: 4063-4067. [85] 宋文宣, 彭代锋. 一种改进全卷积网络的遥感影像变化检测[J]. 遥感信息, 2022, 37(6): 130-136. SONG W X, PENG D F. An improved fully convolutional network for remote sensing imagery change detection[J]. Remote Sensing Information, 2022, 37(6): 130-136. [86] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the Annual Conference on Neural Information Processing Systems, 2014:?2672-2680. [87] LIU J F, CHEN K M, XU G L, et al. Semi-supervised change detection based on graphs with generative adversarial networks[C]//Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 74-77. [88] YANG S, HOU S, ZHANG Y, et al. Change detection of high-resolution remote sensing image based on semi-supervised segmentation and adversarial learning[C]//Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 1055-1058. [89] WU C, DU B, ZHANG L. Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45: 9774-9788. [90] 黄友菊, 农志铣, 韩广萍, 等. 一种基于差异注意力的广西耕地“非粮化”提取研究[J]. 测绘科学, 2023, 48(8): 193-201. HUANG Y J, NONG Z M, HAN G P, et al. Study on the non-grain conversion of cultivated land in guangxi by deep learning change detection. [J]. Surveying and Mapping Science, 2023, 48(8): 193-201. [91] XU C, YE Z Y, MEI L Y, et al. Cross-attention guided group aggregation network for cropland change detection[J]. IEEE Sensors Journal, 2023, 23: 13680-13691. [92] 于海洋, 滑志华, 宋草原, 等. 一种融合多尺度混合注意力的建筑物变化检测模型[J]. 测绘工程, 2024, 33(1): 47-56. YU H Y, HUA Z H, SONG C Y, et al. A building change detection model fusing multi-scale hybrid attention[J]. Surveying and Mapping Engineering, 2024, 33(1): 47-56. [93] TANG W, WU K, ZHANG Y, et al. A siamese network based on multiple attention and multilayer transformers for change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-15. [94] CHEN L, CHEN P X, LIU H P, et al. Change detection for high-resolution remote sensing images based on a unet-like siamese-structured transformer network[J]. Sensors and Materials, 2023, 35(1). [95] BANDARA W G C, PATEL V M. A transformer-based siamese network for change detection[C]//Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 207-210. [96] WU Z, CHEN Y, MENG X, et al. SwinUCDNet: a unet-like network with union attention for cropland change detection of aerial images[C]//Proceedings of the 30th International Conference on Geoinformatics, London, United Kingdom, 2023: 1-7. [97] WANG W H, XIE E Z, LI X, et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021: 548-558. [98] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[C]//Proceedings of the 9th International Conference on Learning Representations, 2021. [99] LIU Z, LIN Y T, CAO Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 2021: 9992-10002. [100] XIE E, WANG W, YU Z, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[C]//Proceedings of the Annual Conference on Neural Information Processing Systems, 2021. [101] SU H, YE Y, HUA Wet al. SASFormer: transformers for sparsely annotated semantic segmentation[C]//Proceedings of the 2023 IEEE International Conference on Multimedia and Expo, Brisbane, Australia, 2023: 390-395. [102] XU X, LI J, CHEN Z. TCIANet: transformer-based context information aggregation network for remote sensing image change detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 1951-1971. [103] PANG S, LAN J, ZUO Z. et al. SFGT-CD: semantic feature-guided building change detection from bitemporal remote-sensing images with transformers[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1-5. [104] GU A, DAO T. Mamba: linear-time sequence modeling with selective state spaces[J]. arXiv:2312.00752, 2023. [105] CHEN H, SONG J, HAN C, et al. ChangeMamba: remote sensing change detection with spatio-temporal state space model[J]. arXiv:2404.03425, 2024. [106] ZHU Q, CAI Y, FANG Y, et al. Samba: semantic segmentation of remotely sensed images with state space model[J]. arXiv:2404.01705, 2024. [107] PENG S, ZHU X, DENG H, et al. FusionMamba: efficient image fusion with state space model[J]. arXiv:2404.07932, 2024. [108] LIU Y, PANG C, ZHAN Z, et al. Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): ?811-815. [109] FANG S, LI K, SHAO J, et al. SNUNet-CD: a densely connected siamese network for change detection of VHR images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5. [110] LI Z, CAO S Y, DENG J K, et al. STADE-CDNet: spatial-temporal attention with difference enhancement- based network for remote sensing image change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-17. [111] SONG S, ZHANG Y, YUAN Y. Iterative edge enhancing framework for building change detection[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1-5. [112] YAN T, WAN Z, ZHANG P, et al. TransY-Net: learning fully transformer networks for change detection of remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-12. [113] 董晨, 郑禄, 于舒, 等. 基于上下文感知与多尺度注意力的遥感变化检测[J]. 软件导刊, 2023, 22(11): 65-70. DONG C, ZHENG L, YU S, et al. Remote sensing change detection based on context perception and multi-scale attention[J]. Software Guide, 2023, 22(11): 65-70. [114] 汪闽, 张星月. 多特征证据融合的遥感图像变化检测[J]. 遥感学报, 2010, 14(3): 558-570. WANG M, ZHANG X Y. Change detection using high spatial resolution remotely sensed imagery by combining evidence theory and structural similarity[J]. Journal of Remote Sensing, 2010, 14 (3): 558-570. [115] BENKOUIDER Y K, KAROUI M S. An unmixing-based change detection approach for multiresolution remote sensing images[C]//Proceedings of the 27th European Signal Processing Conference, A Coruna, Spain, 2019: 1-5. |
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