计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (20): 30-48.DOI: 10.3778/j.issn.1002-8331.2404-0392
孙剑明,赵梦鑫,郝旭耀
出版日期:
2024-10-15
发布日期:
2024-10-15
SUN Jianming, ZHAO Mengxin, HAO Xuyao
Online:
2024-10-15
Published:
2024-10-15
摘要: 遥感图像变化检测是遥感领域中一项重要的研究内容,其旨在利用遥感技术和图像处理方法来识别地表覆盖变化的模式和趋势。为了深入了解该方面的发展现状及其使用的技术方法,总结分析了大量的资料和文献,对遥感图像变化检测方法进行了较为全面的综述。介绍了变化检测的概念和处理流程;从6个角度总结了变化检测方法的分类体系,并回顾了其发展历程;概述了各类变化检测方法的原理和特点,对其优缺点进行了简要分析,从6个方面讨论了对遥感图像进行变化检测的现实应用价值;对存在的一些问题与不足进行了简要分析,提出了可能改善这些问题的方法,同时也预言了这些方法在实际应用中可能会遇到的阻碍。最后,对变化检测方法进行总结,并展望了未来的发展方向,以期更好地了解遥感图像变化检测方法的研究现状和发展趋势,为进一步的研究提供参考。
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.
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.
[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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