多模态遥感图像匹配方法综述

doi:10.11947/j.AGCS.2022.20220126

摘要/Abstract

摘要： 遥感图像匹配是遥感图像处理的关键基础，一直是国内外学者研究的热点。由于多模态图像具有辐射差异、几何差异、尺度差异、视角差异、维度差异等特性，目前尚未出现一种普适性强的通用匹配方法。随着遥感、人工智能、大数据等技术的不断发展和应用领域的持续拓展，图像匹配技术体系也在不断地发展和演化。本文在系统梳理图像匹配技术发展历程的基础上，对多模态遥感图像匹配分类体系进行了归纳总结，从特征驱动和数据驱动两方面论述了多模态图像匹配技术研究的最新进展，并指出其面临的核心困难及未来发展趋势，以期推动多模态图像匹配研究更加深入发展。

关键词: 图像匹配, 多模态, 特征描述, 深度学习

Abstract: Remote sensing image matching is the key foundation of remote sensing image processing, which has been a research hotspot for scholars at home and abroad. Due to the characteristics of multi-modal images such as radiation difference, geometric difference, scale difference, viewpoint difference and dimensional difference, a universal matching method with strong universality has not yet appeared. With the continuous development of remote sensing, artificial intelligence, big data, and other technologies and the continuous expansion of application fields, the image matching technology system is also developing and evolving. This paper summarizes the multi-modal remote sensing image matching classification system based on the development history of image matching technology, discusses the latest progress of multi-modal image matching technology from the perspective of feature-driven and data-driven, and points out the core difficulties and future development trend in order to promote the deeper development of multi-modal image matching research.

Key words: image matching, multi-modal, feature description, deep learning

中图分类号:

P237

眭海刚, 刘畅, 干哲, 江政杰, 徐川. 多模态遥感图像匹配方法综述[J]. 测绘学报, 2022, 51(9): 1848-1861.

SUI Haigang, LIU Chang, GAN Zhe, JIANG Zhengjie, XU Chuan. Overview of multi-modal remote sensing image matching methods[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1848-1861.

参考文献

[1] 贾迪, 朱宁丹, 杨宁华, 等. 图像匹配方法研究综述[J]. 中国图象图形学报, 2019, 24(5):677-699. JIA Di, ZHU Ningdan, YANG Ninghua, et al. Image matching methods[J]. Journal of Image and Graphics, 2019, 24(5):677-699.
[2] CHEN Lin, ROTTENSTEINER F, HEIPKE C. Feature detection and description for image matching:from hand-crafted design to deep learning[J]. Geo-Spatial Information Science, 2021, 24(1):58-74.
[3] DAWN S, SAXENA V, SHARMA B. Remote sensing image registration techniques:a survey[C]//Proceedings of 2010 International Conference on Image and Signal Processing. Berlin,Germany:Springer, 2010:103-112.
[4] 李德仁, 张良培, 夏桂松. 遥感大数据自动分析与数据挖掘[J]. 测绘学报, 2014, 43(12):1211-1216. LI Deren, ZHANG Liangpei, XIA Guisong. Automatic analysis and mining of remote sensing big data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12):1211-1216.
[5] ZHANG Jixian. Multi-source remote sensing data fusion:status and trends[J]. International Journal of Image and Data Fusion, 2010, 1(1):5-24.
[6] YANG Zhuoqian, DAN Tingting, YANG Yang. Multi-temporal remote sensing image registration using deep convolutional features[J]. IEEE Access, 2018, 6:38544-38555.
[7] JIANG Xingyu, MA Jiayi, XIAO Guobao, et al. A review of multimodal image matching:methods and applications[J]. Information Fusion, 2021, 73:22-71.
[8] ZHANG Xinyue, LENG Chengcai, HONG Yameng, et al. Multimodal remote sensing image registration methods and advancements:a survey[J]. Remote Sensing, 2021, 13(24):5128.
[9] YAO Yongxiang, ZHANG Yongjun, WAN Yi, et al. Multi-modal remote sensing image matching considering Co-occurrence filter[J]. IEEE Transactions on Image Processing, 2022, 31:2584-2597.
[10] XIE Xunwei, ZHANG Yongjun, LING Xiao, et al. A novel extended phase correlation algorithm based on Log-Gabor filtering for multimodal remote sensing image registration[J]. International Journal of Remote Sensing, 2019, 40(14):5429-5453.
[11] YI K M, TRULLS E, LEPETIT V, et al. Lift:learned invariant feature transform[C]//Proceedings of 2016 European Conference on Computer Vision. Cham,Germany:Springer, 2016:467-483.
[12] YE Yuanxin, YANG Chao, ZHANG Jiacheng, et al. Optical-to-SAR image matching using multiscale masked structure features[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.
[13] CHEN H M, ARORA M K, VARSHNEY P K. Mutual information-based image registration for remote sensing data[J]. International Journal of Remote Sensing, 2003, 24(18):3701-3706.
[14] XIANG Yuming, TAO Rongshu, WAN Ling, et al. OS-PC:combining feature representation and 3D phase correlation for subpixel optical and SAR image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9):6451-6466.
[15] MA Jiayi, JIANG Xingyu, FAN Aoxiang, et al. Image matching from handcrafted to deep features:a survey[J]. International Journal of Computer Vision, 2021, 129(1):23-79.
[16] ZITOVÁ B, FLUSSER J. Image registration methods:a survey[J]. Image and Vision Computing, 2003, 21(11):977-1000.
[17] LENG Chengcai, ZHANG Hai, LI Bo, et al. Local feature descriptor for image matching:a survey[J]. IEEE Access, 2018,7:6424-6434.
[18] YE Yuanxin, TANG Tengfeng, ZHU Bai, et al. A multiscale framework with unsupervised learning for remote sensing image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15.
[19] GÓMEZ-CHOVA L, TUIA D, MOSER G, et al. Multimodal classification of remote sensing images:a review and future directions[J]. Proceedings of the IEEE, 2015, 103(9):1560-1584.
[20] ARAR M, GINGER Y, DANON D, et al. Unsupervised multi-modal image registration via geometry preserving image-to-image translation[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle, WA, USA:IEEE, 2020:13410-13419.
[21] LIU Xiaoping, CHEN Shuli, ZHUO Li, et al. Multi-sensor image registration by combining local self-similarity matching and mutual information[J]. Frontiers of Earth Science, 2018, 12(4):779-790.
[22] MA Wenping, WU Yue, LIU Shaodi, et al. Remote sensing image registration based on phase congruency feature detection and spatial constraint matching[J]. IEEE Access, 2018, 6:77554-77567.
[23] ZENG Qiang, DU Jianhua, LIU Jiexin, et al. Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT[J]. Journal of Real-Time Image Processing, 2020, 17(5):1103-1115.
[24] 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.
[25] FAN Jianwei, WU Yan, LI Ming, et al. SAR and optical image registration using nonlinear diffusion and phase congruency structural descriptor[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5368-5379.
[26] LOWE D G. Object recognition from local scale-invariant features[C]//Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999,1150-1157.
[27] BAY H, TUYTELAARS T, GOOL L V. Surf:speeded up robust features[C]//Proceedings of 2006 European Conference on Computer Vision. Berlin,Germany:Springer, 2006:404-417.
[28] 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.
[29] XU Chuan, LIU Chang, LI Hongli, et al. Multiview image matching of optical satellite and UAV based on a joint description neural network[J]. Remote Sensing, 2022, 14(4):838.
[30] ZENG Liang, DU Yanlei, LIN Huiping, et al. A novel region-based image registration method for multisource remote sensing images via CNN[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020,14:1821-1831.
[31] XIANG Yuming, WANG Feng, WAN Ling, et al. OS-flow:a robust algorithm for dense optical and SAR image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9):6335-6354.
[32] USS M L, VOZEL B, LUKIN V V, et al. Multimodal remote sensing image registration with accuracy estimation at local and global scales[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(11):6587-6605.
[33] 张祖勋. 新的核线相关算法:跨接法[J]. 武汉测绘科技大学学报, 1988, 13(4):19-27. ZHANG Zuxun. A new approach of epipolar-line image marching-bridging mode[J]. Journal of Wuhan Technical University of Surveying and Mapping, 1988, 13(4):19-27.
[34] EMERY W J, BALDWIN D, MATTHEWS D. Maximum cross correlation automatic satellite image navigation and attitude corrections for open-ocean image navigation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(1):33-42.
[35] 陆和平, 高磊. 基于互相关的多源遥感图像匹配的改进算法[J]. 航天控制, 2009, 27(2):18-21, 25. LU Heping, GAO Lei. Improved algorithm for multi-source remote sensing images based on cross correlation[J]. Aerospace Control, 2009, 27(2):18-21, 25.
[36] MAES F, COLLIGNON A, VANDERMEULEN D, et al. Multimodality image registration by maximization of mutual information[J]. IEEE Transactions on Medical Imaging, 1997, 16(2):187-198.
[37] JOHNSON K, COLE-RHODES A, ZAVORIN I, et al. Mutual information as a similarity measure for remote sensing image registration[C]//Proceedings of 2001 Geo-spatial Image and Data Exploitation II. Orlando, FL,USA:SPIE, 2001, 4383:51-61.
[38] LE MOIGNE J, CAMPBELL W J, CROMP R F. An automated parallel image registration technique based on the correlation of wavelet features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8):1849-1864.
[39] LANCHANTIN P, PIECZYNSKI W.Unsupervised non stationary image segmentation using triplet Markov chains[C]//Proceedings of 2004 Advanced Concepts for Intelligent Vision Systems (ACVIS 04).[S.l.]:IEEE,2004.
[40] 蔡潇. 基于相位特征的异源图像匹配算法[J]. 光学与光电技术, 2021, 19(1):48-53. CAI Xiao. Heterogeneous image matching algorithm based on phase feature[J]. Optics & Optoelectronic Technology, 2021, 19(1):48-53.
[41] 王新生, 孙润德, 姚统. 基于相位一致性的遥感图像匹配方法[J]. 计算机应用, 2021, 41(S1):225-229. WANG Xinsheng, SUN Runde, YAO Tong. Remote sensing image matching algorithm based on phase congruency[J]. Journal of Computer Applications, 2021, 41(S1):225-229.
[42] WONG A, ORCHARD J. Efficient FFT-accelerated approach to invariant optical-LiDAR registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11):3917-3925.
[43] 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.
[44] 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.
[45] FAN Zhongli, ZHANG Li, LIU Yuxuan, et al. Exploiting high geopositioning accuracy of SAR data to obtain accurate geometric orientation of optical satellite images[J]. Remote Sensing, 2021, 13(17):3535.
[46] MORAVEC H P. Techniques towards automatic visual obstacle avoidance[C]//Proceedings of 1997 International Joint Conference on Artificial Intelligence.[S.l.]:IEEE,1977:584.
[47] HE Wei, DENG Xiaolian. A modified SUSAN corner detection algorithm based on adaptive gradient threshold for remote sensing image[C]//Proceedings of 2010 International Conference on Optoelectronics and Image Processing. Haikou, China:IEEE, 2010:40-43.
[48] DURGAM U K, PAUL S, PATI U C. SURF based matching for SAR image registration[C]//Proceedings of 2016 IEEE Students's Conference on Electrical, Electronics and Computer Science. Bhopal, India:IEEE,2016:1-5.
[49] FIRMENICHY D, BROWN M, SVSSTRUNK S. Multispectral interest points for RGB-NIR image registration[C]//Proceedings of the 18th IEEE International Conference on Image Processing. Brussels, Belgium:IEEE, 2011:181-184.
[50] ZHENG Yi, ZHENG Ping. Image matching based on Harris-affine detectors and translation parameter estimation by phase correlation[C]//Proceedings of the 4th IEEE International Conference on Signal and Image Processing. Wuxi, China:IEEE,2019:106-111.
[51] LIU Xiangzeng, AI Yunfeng, ZHANG Juli, et al. A novel affine and contrast invariant descriptor for infrared and visible image registration[J]. Remote Sensing, 2018, 10(4):658.
[52] 刘妍,余淮,杨文, 等.利用SAR-FAST角点检测的合成孔径雷达图像配准方法[J].电子与信息学报,2017,39(2):430-436. LIU Yan, YU Huai, YANG Wen, et al. SAR image registration using SAR-FAST corner detection[J]. Journal of Electronics & Information Technology, 2017, 39(2):430-436.
[53] RAN Li, LIN Luo, YU Zhang. Multiframe astronomical image registration based on block homography estimation[J]. Journal of Sensors, 2020, 2020:1-19.
[54] 王峰, 尤红建, 傅兴玉. 应用于SAR图像配准的自适应SIFT特征均匀分布算法[J]. 武汉大学学报(信息科学版), 2015, 40(2):159-163. WANG Feng, YOU Hongjian, FU Xingyu. Auto-adaptive well-distributed scale-invariant feature for SAR images registration[J]. Geomatics and Information Science of Wuhan University, 2015, 40(2):159-163.
[55] LI Yang, LIU Lingshan, WANG Lianghao, et al. Fast SIFT algorithm based on Sobel edge detector[C]//Proceedings of the 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). Yichang, China:IEEE,2012:1820-1823.
[56] GUO Qing, HE Mengmeng, LI An. High-resolution remote-sensing image registration based on angle matching of edge point features[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(8):2881-2895.
[57] YU Qiuze, ZHOU Shan, JIANG Yuxuan, et al. High-performance SAR image matching using improved SIFT framework based on rolling guidance filter and ROEWA-powered feature[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(3):920-933.
[58] FAN Zhongli, LIU Yuxian, LIU Yuxuan, et al. 3MRS:an effective coarse-to-fine matching method for multimodal remote sensing imagery[J]. Remote Sensing, 2022, 14(3):478.
[59] 张雍吉, 范晋湘, 段连飞. 基于区域特征的光学图像与SAR图像配准算法[J]. 合肥学院学报(自然科学版), 2008, 18(4):37-40. ZHANG Yongji, FAN Jinxiang, DUAN Lianfei. Image registration algorithm for optical and SAR images based on image feature[J]. Journal of Hefei University (Natural Sciences Edition), 2008, 18(4):37-40.
[60] 李雨谦, 皮亦鸣, 王金峰. 基于水平集的SAR图像与光学图像的配准[J]. 测绘学报, 2010, 39(3):276-282. LI Yuqian, PI Yiming, WANG Jinfeng. The registration between SAR and optical image based on level set[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(3):276-282.
[61] 李晓明, 郑链, 胡占义. 基于SIFT特征的遥感影像自动配准[J]. 遥感学报, 2006, 10(6):885-892. LI Xiaoming, ZHENG Lian, HU Zhanyi. SIFT based automatic registration of remotely-sensed imagery[J]. Journal of Remote Sensing, 2006, 10(6):885-892.
[62] MA Wenping, WEN Zelian, WU Yue, et al. Remote sensing image registration with modified SIFT and enhanced feature matching[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(1):3-7.
[63] 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.
[64] 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.
[65] 姚永祥,张永军,万一, 等.顾及各向异性加权力矩与绝对相位方向的异源影像匹配[J].武汉大学学报(信息科学版),2021,46(11):1727-1736. YAO Yongxiang, ZHANG Yongjun, WAN Yi, et al. Heterologous images matching considering anisotropic weighted moment and absolute phase orientation[J].Geomatics and Information Science of Wuhan University, 2021, 46(11):1727-1736.
[66] 段汕, 王小凡, 张洪. 图像相似性度量方法的研究[J]. 中南民族大学学报(自然科学版), 2016, 35(4):121-125. DUAN Shan, WANG Xiaofan, ZHANG Hong. Research on method of similarity measure for images[J]. Journal of South-Central University for Nationalities (Natural Science Edition), 2016, 35(4):121-125.
[67] YE Yuanxin, SHAN Jie, HAO Siyuan, et al. A local phase based invariant feature for remote sensing image matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142:205-221.
[68] FAN Jianwei, WU Yan, LI Ming, et al. SAR and optical image registration using nonlinear diffusion and phase congruency structural descriptor[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5368-5379.
[69] 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.
[70] HU Han, ZHU Qing, DU Zhiqiang, et al. Reliable spatial relationship constrained feature point matching of oblique aerial images[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(1):49-58.
[71] 肖雄武, 郭丙轩, 潘飞, 等. 利用泰勒展开的点特征子像素定位方法[J]. 武汉大学学报(信息科学版), 2014, 39(10):1231-1235. XIAO Xiongwu, GUO Bingxuan, PAN Fei, et al. Sub-pixel location of feature point based on Taylor expansion and its application[J]. Geomatics and Information Science of Wuhan University, 2014, 39(10):1231-1235.
[72] 闫利, 王紫琦, 叶志云. 顾及灰度和梯度信息的多模态影像配准算法[J]. 测绘学报, 2018, 47(1):71-81.DOI:10.11947/j.AGCS.2018.20170368. YAN Li, WANG Ziqi, YE Zhiyun. Multimodal image registration algorithm considering grayscale and gradient information[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1):71-81.DOI:10.11947/j.AGCS.2018.20170368.
[73] SUI Haigang, XU Chuan, LIU Junyi, et al. Automatic optical-to-SAR image registration by iterative line extraction and voronoi integrated spectral point matching[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11):6058-6072.
[74] MA Wenping, LI Na, ZHU Hao, et al. A collaborative correlation-matching network for multimodality remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-18.
[75] 龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报,2018,47(6):693-704. DOI:10.11947/j.AGCS.2018.20170640 GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):693-704. DOI:10.11947/j.AGCS.2018.20170640
[76] 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 (CVPR). Long Beach, CA, USA:IEEE, 2019:8084-8093.
[77] 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.
[78] MA Wenping, ZHANG Jun, WU Yue, et al. A novel two-step registration method for remote sensing images based on deep and local features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7):4834-4843.
[79] 南轲, 齐华, 叶沅鑫. 深度卷积特征表达的多模态遥感影像模板匹配方法[J]. 测绘学报, 2019, 48(6):727-736.DOI:10.11947/j.AGCS.2019.20180432. NAN Ke, QI Hua, YE Yuanxin. A template matching method of multimodal remote sensing images based on deep convolutional feature representation[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6):727-736.DOI:10.11947/j.AGCS.2019.20180432.
[80] 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.
[81] LI Zeyi, ZHANG Haitao, HUANG Yihang. A rotation-invariant optical and SAR image registration algorithm based on deep and Gaussian features[J]. Remote Sensing, 2021, 13(13):2628.
[82] USS M, VOZEL B, LUKIN V, et al. Exhaustive search of correspondences between multimodal remote sensing images using convolutional neural network[J]. Sensors, 2022, 22(3):1231.
[83] 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 (CVPR). Seattle, WA, USA:IEEE, 2020:4937-4946.
[84] MA Jiayi, JIANG Xingyu, JIANG Junjun, et al. LMR:learning a two-class classifier for mismatch removal[J]. IEEE Transactions on Image Processing, 2019, 28(8):4045-4059.
[85] 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.
[86] SUN Jiaming, SHEN Zehong, WANG Yuang, et al. LoFTR:detector-free local feature matching with transformers[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA:IEEE, 2021:8918-8927.
[87] 余佩伦, 施佺, 王晗. 并行生成网络的红外-可见光图像转换[J]. 中国图象图形学报, 2021, 26(10):2346-2356. YU Peilun, SHI Quan, WANG Han. Infrared-to-visible image translation based on parallel generator network[J]. Journal of Image and Graphics, 2021, 26(10):2346-2356.
[88] 尹梦晓, 林振峰, 杨锋. 基于动态感受野的自适应多尺度信息融合的图像转换[J]. 电子与信息学报, 2021, 43(8):2386-2394. YIN Mengxiao, LIN Zhenfeng, YANG Feng. Adaptive multi-scale information fusion based on dynamic receptive field for image-to-image translation[J]. Journal of Electronics & Information Technology, 2021, 43(8):2386-2394.
[89] DU Wenliang, ZHOU Yong, ZHAO Jiaqi, et al. K-means clustering guided generative adversarial networks for SAR-optical image matching[J]. IEEE Access, 2020, 8:217554-217572.
[90] 郭正胜, 李参海. 基于深度学习的遥感图像匹配方法[J]. 测绘与空间地理信息, 2019, 42(1):138-141, 146. GUO Zhengsheng, LI Canhai. Remote sensing image matching method based on depth learning[J]. Geomatics & Spatial Information Technology, 2019, 42(1):138-141, 146.
[91] MA Jiayi, JIANG Junjun, ZHOU Huabing, et al. Guided locality preserving feature matching for remote sensing image registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8):4435-4447.
[92] WANG G, CHEN Y. Robust feature matching using guided local outlier factor[J]. Pattern Recognition, 2021, 117(2):107986.
[93] FISCHLER M A, BOLLES R C. Random sample consensus[J]. Communications of the ACM, 1981, 24(6):381-395.
[94] WANG Gang, CHEN Yufei. Robust feature matching using guided local outlier factor[J]. Pattern Recognition, 2021, 117:107986.
[95] MENG Fanyang, LI Xia, PEI Jihong. A feature point matching based on spatial order constraints bilateral-neighbor vote[J]. IEEE Transactions on Image Processing, 2015, 24(11):4160-4171.
[96] MA Jiayi, MA Yong, ZHAO Ji, et al. Image feature matching via progressive vector field consensus[J]. IEEE Signal Processing Letters, 2015, 22(6):767-771.