测绘学报 ›› 2022, Vol. 51 ›› Issue (6): 862-872.doi: 10.11947/j.AGCS.2022.20220098
郭华东1,2,3, 吴文瑾1,2, 张珂1,2,3, 李新武1,2
收稿日期:
2022-02-16
修回日期:
2022-04-15
发布日期:
2022-07-02
通讯作者:
吴文瑾
E-mail:wuwj@aircas.ac.cn
作者简介:
郭华东(1950-),研究员,中国科学院院士、俄罗斯科学院外籍院士、芬兰科学与人文院外籍院士、发展中国家科学院院士,长期从事空间地球信息科学及雷达对地观测研究。E-mail:hdguo@aircas.ac.cn
基金资助:
GUO Huadong1,2,3, WU Wenjin1,2, ZHANG Ke1,2,3, LI Xinwu1,2
Received:
2022-02-16
Revised:
2022-04-15
Published:
2022-07-02
Supported by:
摘要: 合成孔径雷达(SAR)系统在对地观测中具有全天时全天候的独特优势。近十几年来,多模式、多角度、多维度、大幅宽、高分辨率、多基协同等SAR技术的问世,代表着新型SAR观测时代的到来。为对这一SAR发展阶段的特点和能力进行分析,本文首先介绍了新型SAR系统观测能力的发展,包括如何获取大范围、多时相、多层次SAR综合对地观测数据及实现月基SAR等观测技术;然后,总结了杂交介质建模、时频分解、深度学习、压缩感知等新型信息提取方法在SAR领域发挥的作用;最后,介绍了新型SAR在城市管理、植被调查、极地与海洋测绘以及灾害监测等领域的研究进展,旨在推动SAR观测技术在测绘领域更广泛而深入的应用。
中图分类号:
郭华东, 吴文瑾, 张珂, 李新武. 新型SAR对地环境观测[J]. 测绘学报, 2022, 51(6): 862-872.
GUO Huadong, WU Wenjin, ZHANG Ke, LI Xinwu. New generation SAR for Earth environment observation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 862-872.
[1] 梁泽浩, 王晋, 李广雪. 星载SAR技术的发展及应用浅析[J]. 测绘与空间地理信息, 2021, 44(2):29-32. LIANG Zehao, WANG Jin, LI Guangxue. Brief analysis on SAR technology and application of spaceborne SAR[J]. Geomatics & Spatial Information Technology, 2021, 44(2):29-32. [2] 郭华东, 李新武, 傅文学. 新型SAR地球环境观测[M]. 北京:高等教育出版社, 2020. GUO Huadong, LI Xinwu, FU Wenxue. New generation SAR for earth environment observation[M]. Beijing:Higher Education Press, 2020. [3] 张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3):269-277.DOI:10.11947/j.AGCS.2017.20170049. ZHANG Qingjun. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3):269-277.DOI:10.11947/j.AGCS.2017.20170049. [4] 楼良盛, 刘志铭, 张昊, 等. 天绘二号卫星工程设计与实现[J]. 测绘学报, 2020, 49(10):1252-1264.DOI:10.11947/j.AGCS.2020.20200175. LOU Liangsheng, LIU Zhiming, ZHANG Hao, et al. TH-2 satellite engineering design and implementation[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10):1252-1264.DOI:10.11947/j.AGCS.2020.20200175. [5] TORRES R, SNOEIJ P, GEUDTNER D, et al. GMES sentinel-1 mission[J]. Remote Sensing of Environment, 2012, 120:9-24. [6] TOAN T L, QUEGAN S, DAVIDSON M W J, et al. The BIOMASS mission:mapping global forest biomass to better understand the terrestrial carbon cycle[J]. Remote Sensing of Environment, 2011, 115(11):2850-2860. [7] MOREIRA A, KRIEGER G, HAJNSEK I, et al. Tandem-L:a highly innovative bistatic SAR mission for global observation of dynamic processes on the earth's surface[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(2):8-23. [8] THOMPSON A A. Overview of the RADARSAT constellation mission[J]. Canadian Journal of Remote Sensing, 2015, 41(5):401-407. [9] 李新武, 郭华东, 彭星, 等. SAR对地观测技术及应用新进展[J]. 南京信息工程大学学报(自然科学版), 2020, 12(2):170-180. LI Xinwu, GUO Huadong, PENG Xing, et al. New advances of SAR and its application in earth observation[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2020, 12(2):170-180. [10] CURRIE A, BROWN M A. Wide-swath SAR[J]. IEE Proceedings F Radar and Signal Processing, 1992, 139(2):122. [11] KRIEGER G, GEBERT N, MOREIRA A. Multidimensional waveform encoding:a new digital beamforming technique for synthetic aperture radar remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1):31-46. [12] LI Zhenfang, WANG Hongyang, SU Tao, et al. Generation of wide-swath and high-resolution SAR images from multichannel small spaceborne SAR systems[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(1):82-86. [13] KRIEGER G, GEBERT N, MOREIRA A. Unambiguous SAR signal reconstruction from nonuniform displaced phase center sampling[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(4):260-264. [14] VILLANO M, KRIEGER G, MOREIRA A. Staggered SAR:high-resolution wide-swath imaging by continuous PRI variation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7):4462-4479. [15] KRIEGER G. MIMO-SAR:opportunities and pitfalls[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5):2628-2645. [16] WANG Wenqin. MIMO SAR OFDM chirp waveform diversity design with random matrix modulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1615-1625. [17] CERUTTI-MAORI D, SIKANETA I, KLARE J, et al. MIMO SAR processing for multichannel high-resolution wide-swath radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8):5034-5055. [18] 李财品, 何明一. 地球同步轨道SAR凝视成像变脉冲重复频率技术[J]. 电子科技大学学报, 2016, 45(6):917-922. LI Caipin, HE Mingyi. The technology of pulse repetition frequency variation for geosynchronous orbit SAR with staring imaging[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(6):917-922. [19] BRAUN H M, MERKLE F. The new German high-resolution SAR reconnaissance system started its 10-year operations[C]//Proceedings of the SPIE 7330, Sensors and Systems for Space Applications Ⅲ. Orlando, Florida, USA:SPIE,2009, 7330:9-14. [20] 王辉, 赵凤军, 邓云凯. 毫米波合成孔径雷达的发展及其应用[J]. 红外与毫米波学报, 2015, 34(4):452-459. WANG Hui, ZHAO Fengjun, DENG Yunkai. Development and application of the millimeter wave SAR[J]. Journal of Infrared and Millimeter Waves, 2015, 34(4):452-459. [21] MAGNARD C, BREHM T, ESSEN H, et al. High resolution MEMPHIS SAR data processing and applications[C]//Proceedings of the Electromagnetics Research Symposium Proceedings. Kuala Lumpur,Malaysia:Electromagnetics Academy, 2012:328-332. [22] DOERRY A W, DUBBERT D F, THOMPSON M, et al. A portfolio of fine resolution Ka-band SAR images:part Ⅰ[C]//Proceedings of the SPIE 5788, Radar Sensor Technology IX. Orlando, Florida,USA:SPIE, 2005:13-24. [23] WU Wenjin, LI Xinwu, GUO Huadong, et al. Noncircularity parameters and their potential applications in uhr mmw sar data sets[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(10):1547-1551. [24] WU Wenjin, GUO Huadong, LI Xinwu, et al. Urban land use information extraction using the ultrahigh-resolution Chinese airborne SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10):5583-5599. [25] FJORTOFT R, GAUDIN J M, POURTHIé N, et al. KaRIn on SWOT:characteristics of near-nadir Ka-band interferometric SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4):2172-2185. [26] 曹淑敏. 斜视多角度SAR图像配准及高度估计方法研究[D]. 青岛:山东科技大学, 2017. CAO Shumin. Research on squint and mylti-angle image registration and height estimation[D]. Qingdao:Shandong University of Science and Technology, 2017. [27] 周汉飞. 多角度SAR成像及特征提取[D]. 长沙:国防科学技术大学, 2013. ZHOU Hanfei. Multi-aspect SAR imaging and feature extraction[D]. Changsha:National University of Defense Technology, 2013. [28] 别博文. 高速机动平台大斜视SAR宽幅成像算法研究[D]. 西安:西安电子科技大学, 2019. BIE Bowen. Study on high speed maneuvering platforms high squint SAR wide swath imaging algorithm[D]. Xi'an:Xidian University, 2019. [29] 曹淑敏, 吴文瑾, 李新武, 等. 斜视SAR重采样误差分析及改进[J]. 遥感信息, 2017, 32(6):1-7. CAO Shumin, WU Wenjin, LI Xinwu, et al. Resampling error analysis and improvement of squint SAR[J]. Remote Sensing Information, 2017, 32(6):1-7. [30] WU Wenjin, GUO Huadong, LI Xinwu. Urban area SAR image man-made target extraction based on the product model and the time-frequency analysis[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(3):943-952. [31] WU Wenjin, GUO Huadong, LI Xinwu. Man-made target detection in urban areas based on a new azimuth stationarity extraction method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3):1138-1146. [32] PONCE O, PRATS-IRAOLA P, SCHEIBER R, et al. First airborne demonstration of holographic SAR tomography with fully polarimetric multicircular acquisitions at L-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):6170-6196. [33] PONCE O, PRATS P, RODRIGUEZ-CASSOLA M, et al. Processing of circular SAR trajectories with fast factorized back-projection[C]//Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada. IEEE, 2011:3692-3695. [34] LIN Yun, HONG Wen, TAN Weixian, et al. Airborne circular SAR imaging:results at P-band[C]//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. July 22-27, 2012, Munich, Germany:IEEE, 2012:5594-5597. [35] 申文杰, 韩冰, 林赟, 等. 多角度SAR动目标检测技术及其高分三号实验验证研究[J]. 雷达学报, 2020, 9(2):304-320. SHEN Wenjie, HAN Bing, LIN Yun, et al. Multi-aspect SAR-GMTI and experimental research on Gaofen-3 SAR modes[J]. Journal of Radars, 2020, 9(2):304-320. [36] SCHMITT M, STILLA U. Fusion of airborne multi-aspect InSAR data by simultaneous backward geocoding[C]//Proceedings of 2011 Joint Urban Remote Sensing Event. Munich, Germany:IEEE, 2011:53-56. [37] IGNATENKO V, LAURILA P, RADIUS A, et al. ICEYE microsatellite SAR constellation status update:evaluation of first commercial imaging modes[C]//Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa, HI, USA. IEEE, 2020:3581-3584. [38] CASTELLETTI D, FARQUHARSON G, STRINGHAM C, et al. Capella space first operational SAR satellite[C]//Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels, Belgium:IEEE, 2021:1483-1486. [39] SAITO H, TANAKA K, MITA M, et al. Development and orbit demonstration of small synthetic aperture radar satellite[C]//Proceedings of the 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). Bali, Indonesia:IEEE, 2021:1-5. [40] XUE Sihan, GENG Xupu, MENG Lingsheng, et al. HISEA-1:the first C-band SAR miniaturized satellite for ocean and coastal observation[J]. Remote Sensing, 2021, 13(11):2076. [41] POTIN P, ROSICH B, GRIMONT P, et al. Sentinel-1 mission status[C]//Proceedings of the 11th European Conference on Synthetic Aperture Radar. Hamburg,Germany:VDE, 2016:1-6. [42] ZENG Tao, ZHU Mao, HU Cheng, et al. Experimental results and algorithm analysis of DEM generation using bistatic SAR interferometry with stationary receiver[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11):5835-5852. [43] ZHANG Mingmi, WANG R, DENG Yunkai, et al. A synchronization algorithm for spaceborne/stationary BiSAR imaging based on contrast optimization with direct signal from radar satellite[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):1977-1989. [44] 杨建宇. 双基地合成孔径雷达技术[J]. 电子科技大学学报, 2016, 45(4):482-501. YANG Jianyu. Bistatic synthetic aperture radar technology[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4):482-501. [45] FORNARO G, REALE D, PAUCIULLO A, et al. SAR Tomography:an advanced tool for spaceborne 4D radar scanning with application to imaging and monitoring of cities and single buildings[J]. IEEE Geoscience and Remote Sensing Newsletter, 2012,6(1):9-17. [46] BAUMGARTNER S V, KRIEGER G. Large along-track baseline SAR-GMTI:first results with the TerraSAR-X/TanDEM-X satellite constellation[C]//Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada:IEEE, 2011:1319-1322. [47] 郭华东, 张露. 雷达遥感六十年:四个阶段的发展[J]. 遥感学报, 2019, 23(6):1023-1035. GUO Huadong, ZHANG Lu. 60 years of radar remote sensing:four-stage development[J]. Journal of Remote Sensing, 2019, 23(6):1023-1035. [48] 郭华东, 丁翼星, 刘广, 等. 面向全球变化探测的月基成像雷达概念研究[J]. 中国科学:地球科学, 2013, 43(11):1760-1769. GUO Huadong, DING Yixing, LIU Guang, et al. Conceptual study of lunar-based SAR for global change monitoring[J]. Scientia Sinica Terrae, 2013, 43(11):1760-1769. [49] GUO Huadong, FU Wenxue, LIU Guang. Scientific Satellite and Moon-Based Earth Observation for Global Change[M]. Singapore:Springer Singapore, 2019. [50] GUO Huadong, REN Yuanzhen, LIU Guang, et al. The angular characteristics of Moon-based Earth observations[J]. International Journal of Digital Earth, 2020, 13(3):339-354. [51] REN Yuanzhen, GUO Huadong, LIU Guang, et al. Simulation study of geometric characteristics and coverage for moon-based earth observation in the electro-optical region[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6):2431-2440. [52] DING Yixing, GUO Huadong, LIU Guang, et al. Constructing a high-accuracy geometric model for moon-based earth observation[J]. Remote Sensing, 2019, 11(22):2611. [53] XU Zhen, CHEN Kunshan. On signal modeling of moon-based synthetic aperture radar (SAR) imaging of earth[J]. Remote Sensing, 2018, 10(3):486. [54] XU Zhen, CHEN Kunshan. Temporal-spatial varying background ionospheric effects on the moon-based synthetic aperture radar imaging:a theoretical analysis[J]. IEEE Access, 2018, 6:66767-66786. [55] 李德伟, 江利明, 蒋厚军, 等. 月基SAR对地观测系统参数分析[J]. 系统工程与电子技术, 2020, 42(4):792-798. LI Dewei, JIANG Liming, JIANG Houjun, et al. System parameters analysis of the Moon-based SAR Earth observation[J]. Systems Engineering and Electronics, 2020, 42(4):792-798. [56] CHEN Guoqiang, GUO Huadong, DING Yixing, et al. Influence of topography on the site selection of a moon-based earth observation station[J]. Sensors (Basel, Switzerland), 2021, 21(21):7198. [57] XU Zhen, CHEN Kunshan. Effects of the Earth's curvature and lunar revolution on the imaging performance of the moon-based synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8):5868-5882. [58] XU Zhen, CHEN Kunshan, ZHOU Guoqing. Effects of the Earth's irregular rotation on the moon-based synthetic aperture radar imaging[J]. IEEE Access, 2019, 7:155014-155027. [59] DONG Jinglong, SHEN Qiang, JIANG Liming, et al. An analysis of spatiotemporal baseline and effective spatial coverage for lunar-based SAR repeat-track interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9):3458-3469. [60] 李德伟, 江利明, 蒋厚军, 等. 固体潮位移InSAR相位模拟及对广域地表形变监测的影响初探[J]. 地球物理学报, 2019, 62(12):4527-4539. LI Dewei, JIANG Liming, JIANG Houjun, et al. InSAR phase simulation of solid earth tide and its influence on surface deformation monitoring at wide-area scale[J]. Chinese Journal of Geophysics, 2019, 62(12):4527-4539. [61] WU Kai, JI Ce, LUO Lei, et al. Simulation study of moon-based InSAR observation for solid earth tides[J]. Remote Sensing, 2020, 12(1):123. [62] 郭华东. 地球系统空间观测:从科学卫星到月基平台[J]. 遥感学报, 2016, 20(5):716-723. GUO Huadong. Earth system observation from space:from scientific satellite to Moonbased platform[J]. Journal of Remote Sensing, 2016, 20(5):716-723. [63] GUO Huadong, LIU Guang, DING Yixing, et al. Moon-based earth observation for large scale geoscience phenomena[C]//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China:IEEE, 2016:3705-3707. [64] GAO Gui. Statistical modeling of SAR images:a survey[J]. Sensors (Basel, Switzerland), 2010, 10(1):775-795. [65] LI Hengchao, HONG Wen, WU Yirong, et al. On the empirical-statistical modeling of SAR images with generalized gamma distribution[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3):386-397. [66] PALM B G, BAYER F M, CINTRA R J, et al. Rayleigh regression model for ground type detection in SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(10):1660-1664. [67] KARAKUŞ O, KURUOĞLU E E, ACHIM A. A generalized Gaussian extension to the rician distribution for SAR image modeling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15. [68] PENNA P A A, MASCARENHAS N D A. SAR speckle nonlocal filtering with statistical modeling of haar wavelet coefficients and stochastic distances[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9):7194-7208. [69] XIANG Deliang, ZHANG Fan, ZHANG Wei, et al. Fast pixel-superpixel region merging for SAR image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11):9319-9335. [70] JING Wenbo, JIN Tian, XIANG Deliang. SAR image edge detection with recurrent guidance filter[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(6):1064-1068. [71] LI Tao, LIU Zheng, RAN Lei, et al. Target detection by exploiting superpixel-level statistical dissimilarity for SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4):562-566. [72] AINSWORTH T L, JANSEN R W, LEE J S, et al. Sub-aperture analysis of high-resolution polarimetric SAR data[C]//Proceedings of 1999 IEEE International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293). Hamburg, Germany:IEEE, 1999:41-43. [73] CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4806-4817. [74] HE Chu, LI Shuang, LIAO Zixian, et al. Texture classification of PolSAR data based on sparse coding of wavelet polarization textons[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(8):4576-4590. [75] CUI Shiyong, SCHWARZ G, DATCU M. Remote sensing image classification:no features, no clustering[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(11):5158-5170. [76] ANGHEL A, VASILE G, IOANA C, et al. Vibration estimation in SAR images using azimuth time-frequency tracking and a matched signal transform[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy:IEEE, 2015:2576-2579. [77] HU Canbin, XIONG Boli, LU Jun, et al. SAR Azimuth ambiguities removal for ship detection using time-frequency techniques[C]//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City, QC, Canada:IEEE, 2014:982-985. [78] MIAN A, OVARLEZ J P, GINOLHAC G, et al. Multivariate change detection on high resolution monovariate SAR image using linear time-frequency analysis[C]//Proceedings of the 25th European Signal Processing Conference (EUSIPCO). Kos, Greece:IEEE, 2017:1942-1946. [79] LYU Qiyuan, HAN Bing, LI Guangzuo, et al. SAR interference suppression algorithm based on low-rank and sparse matrix decomposition in time-frequency domain[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5. [80] ZHANG Lu, HUANG Yue, FERRO-FAMIL L, et al. Effect of polarimetric information on time-frequency analysis using spaceborne SAR image[C]//Proceedings of the 13th European Conference on Synthetic Aperture Radar.[S.l.]:VDE, 2021:1-6. [81] CEXUS J C, TOUMI A. Radar target recognition using time-frequency analysis and polar transformation[C]//Proceedings of the 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). Sousse, Tunisia:IEEE, 2018:1-6. [82] HUANG Zhongling, DUMITRU C O, PAN Zongxu, et al. A novel deep learning framework based on transfer learning and joint time-frequency analysis[C]//TerraSAR-X Science Team Meeting 2019. Oberpfaffenhofen, Germany:[s.n.], 2019. [83] BANDA F, FERRO-FAMIL L, TEBALDINI S. Polarimetric time-frequency analysis of vessels in Spotlight SAR images[C]//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City, QC, Canada:IEEE, 2014:1033-1036. [84] YUE Zhenyu, GAO Fei, XIONG Qingxu, et al. A novel attention fully convolutional network method for synthetic aperture radar image segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:4585-4598. [85] VITALE S, FERRAIOLI G, PASCAZIO V. Multi-objective CNN-based algorithm for SAR despeckling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(11):9336-9349. [86] XIA Junshi, YOKOYA N, ADRIANO B, et al. A benchmark high-resolution GaoFen-3 SAR dataset for building semantic segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:5950-5963. [87] ZHOU Yu, WANG Haipeng, XU Feng, et al. Polarimetric SAR image classification using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12):1935-1939. [88] ZHANG Zhimian, WANG Haipeng, XU Feng, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12):7177-7188. [89] GENG Jie, FAN Jianchao, WANG Hongyu, et al. High-resolution SAR image classification via deep convolutional autoencoders[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11):2351-2355. [90] WU Wenjin, LI Hailei, ZHANG Lu, et al. High-resolution PolSAR scene classification with pretrained deep convnets and manifold polarimetric parameters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10):6159-6168. [91] WU Wenjin, LI Hailei, LI Xinwu, et al. PolSAR image semantic segmentation based on deep transfer learning-realizing smooth classification with small training sets[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6):977-981. [92] SHANNON C E. Communication in the presence of noise[J]. Proceedings of the IEEE, 1984, 72(9):1192-1201. [93] CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-509. [94] YANG Jungang, JIN Tian, XIAO Chao, et al. Compressed sensing radar imaging:fundamentals, challenges, and advances[J]. Sensors (Basel, Switzerland), 2019, 19(14):3100. [95] NI Jiacheng, ZHANG Qun, LUO Ying, et al. Compressed sensing SAR imaging based on centralized sparse representation[J]. IEEE Sensors Journal, 2018, 18(12):4920-4932. [96] POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6):1006-1020. [97] 梁雷. 基于压缩感知的极化层析SAR建筑物与树林三维结构参数反演研究[D]. 北京:中国科学院大学. LIANG Lei.Study of compressive sensing-based polarimetric SAR tomography for inversion of three-dimensional structural parameters of buildings and forests[D].Beijing:University of Chinese Academy of Sciences. [98] CAZCARRA-BES V, PARDINI M, TELLO M, et al. Comparison of tomographic SAR reflectivity reconstruction algorithms for forest applications at L-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1):147-164. [99] LIU Hui, GUO Ziye, PANG Lei, et al. Spatial shift phenomenon compensation for TomoSAR imaging using high-resolution TerraSAR-X data[J]. IEEE Access, 2021, 9:13970-13980. [100] WU Chunxiao, ZHANG Zenghui, CHEN Longyong, et al. Super-resolution for MIMO array SAR 3-D imaging based on compressive sensing and deep neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3109-3124. [101] QUAN Yinghui, ZHANG Rui, LI Yachao, et al. Microwave correlation forward-looking super-resolution imaging based on compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10):8326-8337. [102] LV Qi, DOU Yong, NIU Xin, et al. Urban land use and land cover classification using remotely sensed SAR data through deep belief networks[J]. Journal of Sensors, 2015, 2015:1-10. [103] LI Lu, WANG Chao, ZHANG Hong, et al. Urban building change detection in SAR images using combined differential image and residual U-net network[J]. Remote Sensing, 2019, 11(9):1091. [104] WU Wenjin, LI Xinwu, GUO Huadong, et al. Millimeter-wave ultrahigh resolution SAR image classification based on a new feature set[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(8):1204-1208. [105] ZHU Xiao xiang, MONTAZERI S, GISINGER C, et al. Geodetic SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1):18-35. [106] 毕辉, 金双, 王潇, 等. 基于高分三号SAR数据的城市建筑高分辨率高维成像[J]. 雷达学报, 2022, 11(1):40-51. BI Hui, JIN Shuang, WANG Xiao, et al. High-resolution high-dimensional imaging of urban building based on Gao Fen-3 SAR data[J]. Journal of Radars, 2022, 11(1):40-51. [107] MA Peifeng, LIN Hui. Robust detection of single and double persistent scatterers in urban built environments[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):2124-2139. [108] WANG Zhigui, LIU Mei. Seasonal deformation and accelerated motion of infrastructure monitoring using a generalized differential SAR tomography[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4):626-630. [109] D'ANDRIMONT R, VERHEGGHEN A, LEMOINE G, et al. From parcel to continental scale-A first European crop type map based on Sentinel-1 and LUCAS Copernicus in situ observations[J]. Remote Sensing of Environment, 2021, 266:112708. [110] SANTORO M, CARTUS O, CARVALHAIS N, et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations[J]. Earth System Science Data, 2021, 13(8):3927-3950. [111] BARTSCH A, WIDHALM B, LEIBMAN M, et al. Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data[J]. Remote Sensing of Environment, 2020, 237:111515. [112] SOJA M J, QUEGAN S, D'ALESSANDRO M M, et al. Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data[J]. Remote Sensing of Environment, 2021, 253:112153. [113] CARTUS O, SANTORO M. Exploring combinations of multi-temporal and multi-frequency radar backscatter observations to estimate above-ground biomass of tropical forest[J]. Remote Sensing of Environment, 2019, 232:111313. [114] LAL P, KUMAR A, SAIKIA P, et al. Effect of vegetation structure on above ground biomass in tropical deciduous forests of Central India[J]. Geocarto International, 2021:1-17. [115] ZHANG Lu, LIU Huiying, GU Xinwei, et al. Sea ice classification using TerraSAR-X ScanSAR data with removal of scalloping and interscan banding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(2):589-598. [116] LIANG Dong, GUO Huadong, ZHANG Lu, et al. Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine[J]. Remote Sensing of Environment, 2021, 256:112318. [117] LIANG Dong, GUO Huadong, ZHANG Lu, et al. Sentinel-1 EW mode dataset for Antarctica from 2014-2020 produced by the CAS Earth Cloud Service Platform[J]. Big Earth Data, 2021:1-16. [118] ARTHUR J F, STOKES C, JAMIESON S, et al. Recent understanding of Antarctic supraglacial lakes using satellite remote sensing[J]. Progress in Physical Geography:Earth and Environment, 2020, 44(6):837-869. [119] KING M D, HOWAT I M, CANDELA S G, et al. Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat[J]. Communications Earth & Environment, 2020, 1(1):1-7. [120] HOW P, MESSERLI A, MÄTZLER E, et al. Greenland-wide inventory of ice marginal lakes using a multi-method approach[J]. Scientific reports, 2021, 11(1):1-13. [121] ZHAO Jingjing, LIANG Shuang, LI Xinwu, et al. Detection of surface crevasses over Antarctic ice shelves using SAR imagery and deep learning method[J]. Remote Sensing, 2022, 14(3):487. [122] ZHANG Biao, MOUCHE A, LU Yiru, et al. A geophysical model function for wind speed retrieval from C-band HH-polarized synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(10):1521-1525. [123] LU Yiru, ZHANG Biao, PERRIE W, et al. A C-band geophysical model function for determining coastal wind speed using synthetic aperture radar[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(7):2417-2428. [124] ZHANG Biao, LU Yiru, PERRIE W, et al. Compact polarimetry synthetic aperture radar ocean wind retrieval:model development and validation[J]. Journal of Atmospheric and Oceanic Technology, 2021, 38(4):747-757. [125] ELYOUNCHA A, ERIKSSON L E B, ROMEISER R, et al. Measurements of sea surface currents in the Baltic sea region using spaceborne along-track InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11):8584-8599. [126] LIU Yu, HE Yijun, ZHANG Biao. Ocean wave parameters retrieved directly from compact Polarimetric SAR data[J]. Acta Oceanologica Sinica, 2022, 41(4):129-137. [127] YANG Junxin, YUAN Xinzhe, HAN Bing, et al. Correction:Yang, J., et al. phase imbalance analysis of GF-3 along-track InSAR data and ocean current measurements. remote Sens. 2021, 13, 269[J]. Remote Sensing, 2021, 13(4):540. [128] YAN He, HOU Qianru, JIN Guodong, et al. Velocity estimation of ocean surface currents in along-track InSAR system based on conditional generative adversarial networks[J]. Remote Sensing, 2021, 13(20):4088. [129] RASHID M, GIERULL C H. Retrieval of ocean surface radial velocities with RADARSAT-2 along-track interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:9597-9608. [130] SAGANEITI L, AMATO F, NOLÈ G, et al. Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data:the case of the Amatrice earthquake in central Italy, on 24th August 2016[J]. International Journal of Disaster Risk Reduction, 2020, 51:101924. [131] INTRIERI E, RASPINI F, FUMAGALLI A, et al. The Maoxian landslide as seen from space:detecting precursors of failure with Sentinel-1 data[J]. Landslides, 2018, 15(1):123-133. [132] BAN Y, ZHANG P, NASCETTI A, et al. Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning[J]. Scientific Reports, 2020, 10(1):1-15. [133] 许小华, 黄萍, 黄诗峰, 等. 鄱阳湖洪涝灾害卫星雷达遥感应急监测应用[J]. 中国防汛抗旱, 2021, 31(4):10-14. XU Xiaohua, HUANG Ping, HUANG Shifeng, et al. Application of satellite radar remote sensing for emergency monitoring of flood disasters in Poyang Lake[J]. China Flood & Drought Management, 2021, 31(4):10-14. [134] LIU Jihong, HU Jun, LI Zhiwei, et al. Three-dimensional surface displacements of the 8 January 2022 Mw6.7 Menyuan earthquake, China from sentinel-1 and ALOS-2 SAR observations[J]. Remote Sensing, 2022, 14(6):1404. [135] 胡羽丰, 李振洪, 王乐, 等. 2022年汤加火山喷发的综合遥感快速解译分析[J]. 武汉大学学报(信息科学版), 2022, 47(2):242-251. HU Yufeng, LI Zhenhong, WANG Le, et al. Rapid interpretation and analysis of the 2022 Eruption of Hunga Tonga-Hunga Ha'apai volcano with integrated remote sensing techniques[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2):242-251. |
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