[1] 冯蕊涛, 杜清运, 罗恒, 等. 基于光流校正的复杂地形区多时相遥感影像配准[J]. 遥感学报, 2021, 25(2):630-640. FENG Ruitao, DU Qingyun, LUO Heng, et al. A registration algorithm based on optical flow modification for multi-temporal remote sensing images covering the complex-terrain region[J]. National Remote Sensing Bulletin, 2021, 25(2):630-640. [2] TONG Xinyi, XIA Guisong, HU Fan, et al. Exploiting deep features for remote sensing image retrieval:a systematic investigation[J]. IEEE Transactions on Big Data, 2020, 6(3):507-521. [3] CHENG Jian, LENG Cong, WU Jiaxiang, et al. Fast and accurate image matching with cascade hashing for 3D reconstruction[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus:IEEE, 2014:1-8. [4] BRUNNER D, LEMOINE G, BRUZZONE L. Earthquake damage assessment of buildings using VHR optical and SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5):2403-2420. [5] ZHANG Xu, YU F X, KARAMAN S, et al. Learning discriminative and transformation covariant local feature detectors[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu:IEEE, 2017:4923-4931. [6] FAN Bin, HUO Chunlei, PAN Chunhong, et al. Registration of optical and SAR satellite images by exploring the spatial relationship of the improved SIFT[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4):657-661. [7] XIANG Yuming, WANG Feng, YOU Hongjian. OS-SIFT:a robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6):3078-3090. [8] YU Qiuze, NI Dawen, JIANG Yuxuan, et al. Universal SAR and optical image registration via a novel SIFT framework based on nonlinear diffusion and a polar spatial-frequency descriptor[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 171:1-17. [9] ZHANG Wannan. Combination of SIFT and canny edge detection for registration between SAR and optical images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5. [10] 李培, 姜刚, 马千里, 等. 结合张量与互信息的混合模型多模态图像配准方法[J]. 测绘学报, 2021, 50(7):916-929. DOI:10.11947/j.AGCS.2021.20200492. LI Pei, JIANG Gang, MA Qianli, et al. A hybrid model combining tensor and mutual information for multi-modal image registration[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(7):916-929. DOI:10.11947/j.AGCS.2021.20200492. [11] 叶沅鑫, 单杰, 彭剑威, 等. 利用局部自相似进行多光谱遥感图像自动配准[J]. 测绘学报, 2014, 43(3):268-275. YE Yuanxin, SHAN Jie, PENG Jianwei, et al. Automated multispectral remote sensing image registration using local self-similarity[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(3):268-275. [12] YE Yuanxin, BRUZZONE L, SHAN Jie, et al. Fast and robust matching for multimodal remote sensing image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11):9059-9070. [13] YE Yuanxin, SHAN Jie, BRUZZONE L, et al. Robust registration of multimodal remote sensing images based on structural similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5):2941-2958. [14] 张力, 刘玉轩, 孙洋杰, 等. 数字航空摄影三维重建理论与技术发展综述[J]. 测绘学报, 2022, 51(7):1437-1457. DOI:10.11947/j.AGCS.2022.20220130. ZHANG Li, LIU Yuxuan, SUN Yangjie, et al. A review of developments in the theory and technology of three-dimensional reconstruction in digital aerial photogrammetry[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7):1437-1457. DOI:10.11947/j.AGCS.2022.20220130. [15] 蓝朝桢, 卢万杰, 于君明, 等. 异源遥感影像特征匹配的深度学习算法[J]. 测绘学报, 2021, 50(2):189-202. DOI:10.11947/j.AGCS.2021.20200048. LAN Chaozhen, LU Wanjie, YU Junming, et al. Deep learning algorithm for feature matching of cross modality remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2):189-202. DOI:10.11947/j.AGCS.2021.20200048. [16] FAN Rongbo, HOU Bochuan, LIU Jinbao, et al. Registration of multiresolution remote sensing images based on L2-siamese model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14:237-248. [17] ZHANG Han, NI Weiping, YAN Weidong, et al. Registration of multimodal remote sensing image based on deep fully convolutional neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8):3028-3042. [18] BÜRGMANN T, KOPPE W, SCHMITT M. Matching of TerraSAR-X derived ground control points to optical image patches using deep learning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158:241-248. [19] DUSMANU M, ROCCO I, PAJDLA T, et al. D2-net:a trainable CNN for joint description and detection of local features[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach:IEEE, 2020:8084-8093. [20] SARLIN P E, DETONE D, MALISIEWICZ T, et al. SuperGlue:learning feature matching with graph neural networks[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle:IEEE, 2020:4938-4947. [21] CHEN Hongkai, LUO Zixin, ZHANG Jiahui, et al. Learning to match features with seeded graph matching network[C]//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal:IEEE, 2022:6281-6290. [22] WANG Shuang, QUAN Dou, LIANG Xuefeng, et al. A deep learning framework for remote sensing image registration[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145:148-164. [23] HUGHES L H, MARCOS D, LOBRY S, et al. A deep learning framework for matching of SAR and optical imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169:166-179. [24] 朱庆涛, 殷君君, 曾亮, 等. 基于邻域一致性的极化SAR图像仿射配准[J]. 雷达学报, 2021, 10(1):49-60. ZHU Qingtao, YIN Junjun, ZENG Liang, et al. Polarimetric SAR image affine registration based on neighborhood consensus[J]. Journal of Radars, 2021, 10(1):49-60. [25] ROCCO I, ARANDJELOVIC' R, SIVIC J. Convolutional neural network architecture for geometric matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(11):2553-2567. [26] PARK J H, NAM W J, LEE S W. A two-stream symmetric network with bidirectional ensemble for aerial image matching[J]. Remote Sensing, 2020, 12(3):465. [27] LI Jiayuan, HU Qingwu, AI Mingyao. RIFT:multi-modal image matching based on radiation-variation insensitive feature transform[J]. IEEE Transactions on Image Processing, 2020, 29:3296-3310. |