智能化InSAR数据处理研究进展、挑战与展望

doi:10.11947/j.AGCS.2024.20230440

测绘学报 ›› 2024, Vol. 53 ›› Issue (6): 1037-1056.doi: 10.11947/j.AGCS.2024.20230440

智能化InSAR数据处理研究进展、挑战与展望

江利明¹^,²(), 邵益¹^,², 周志伟¹^,², 马培峰³, 王腾⁴

^1.中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室，湖北　武汉　430077
^2.中国科学院大学地球与行星科学学院，北京　100049
^3.香港中文大学太空与地球信息科学研究所，香港　999077
^4.北京大学地球与空间科学学院，北京　100871

收稿日期:2023-10-07 发布日期:2024-07-22
作者简介:江利明（1976—），男，博士，研究员，研究方向为影像大地测量理论、方法与应用。　E-mail：jlm@apm.ac.cn
基金资助:
国家自然科学基金(42174046);湖北省自然科学基金创新群体项目(2021CFA028);第二次青藏高原综合科学考察研究项目(2019QZKK0905)

A review of intelligent InSAR data processing: recent advancements, challenges and prospects

Liming JIANG¹^,²(), Yi SHAO¹^,², Zhiwei ZHOU¹^,², Peifeng MA³, Teng WANG⁴

^1.State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
^2.College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
^3.Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China
^4.School of Earth and Space Sciences, Peking University, Beijing 100871, China

Received:2023-10-07 Published:2024-07-22
About author:JIANG Liming (1976—), male, PhD, researcher, majors in theory, methods and applications of imaging geodesy.　E-mail: jlm@apm.ac.cn
Supported by:
The National Natural Science Foundation of China(42174046);The Innovative Research Group Project Natural Science Foundation of Hubei Province(2021CFA028);The Second Tibetan Plateau Scientific Expedition and Research (STEP) program(2019QZKK0905)

摘要/Abstract

摘要：

随着海量SAR数据的持续积累及深度学习技术的快速发展，以大数据分析和人工智能为主要特征的智能InSAR时代即将来临。本文综述了深度学习技术在InSAR数据处理中的研究现状与发展趋势。首先，简述了目前主流InSAR数据处理方法，分析了在复杂应用场景下其监测精度、处理效率和自动化程度等方面的局限性。然后，在介绍主要InSAR深度学习网络（包括卷积神经网络、循环神经网络和生成对抗网络）的基础上，根据深度学习技术在InSAR数据处理关键环节中的应用，结合笔者团队研究实践，系统梳理了InSAR相位滤波、相位解缠、PS/DS点选取、大气校正、形变估计和形变预测等方面智能化处理的研究进展。最后，探讨了基于深度学习的InSAR数据智能化处理面临的挑战，并对未来发展趋势进行了展望。

关键词: InSAR, 地表形变, 智能处理, 深度学习, 神经网络

Abstract:

With the continuous accumulation of massive SAR data and the rapid development of deep learning technologies, the era of intelligent InSAR is approaching, mainly characterized by big data analysis and artificial intelligence. This paper provides an overview of recent progress and development trend of InSAR data processing technologies with deep learning. Firstly, the mainstream InSAR data processing methods are briefly described, and their limitations in complex application scenarios are analyzed, in terms of monitoring accuracy, processing efficiency and automation level. Then, on base of introduction of the main deep learning networks used in InSAR data processing, including convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN), we systematically review recent advancements of intelligent InSAR data processing, e.g. phase filtering, phase unwrapping, PS/DS target selection, atmospheric delay correction, deformation estimation and deformation prediction. Finally, we discuss challenges faced by intelligent InSAR data processing based on deep learning, and provides an outlook on future development trends.

Key words: InSAR, ground deformation, intelligent processing, deep learning, neural networks

中图分类号:

P258

江利明, 邵益, 周志伟, 马培峰, 王腾. 智能化InSAR数据处理研究进展、挑战与展望[J]. 测绘学报, 2024, 53(6): 1037-1056.

Liming JIANG, Yi SHAO, Zhiwei ZHOU, Peifeng MA, Teng WANG. A review of intelligent InSAR data processing: recent advancements, challenges and prospects[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1037-1056.

参考文献 37

[1]	陈军, 刘万增, 武昊, 等. 智能化测绘的基本问题与发展方向[J]. 测绘学报, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235.
	CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping: fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235.
[2]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[3]	ELMAN J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2):179-211.
[4]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of 2014 International Conference on Neural Information Processing Systems. Montreal: ACM Press, 2014: 2672-2680.
[5]	MA Lei, LIU Yu, ZHANG Xueliang, 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.
[6]	CHENG Gong, XIE Xingxing, HAN Junwei, et al. Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3735-3756.
[7]	PACIFICI F, DEL FRATE F, SOLIMINI C, et al. An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(9):2940-2952.
[8]	龚健雅, 张觅, 胡翔云, 等. 智能遥感深度学习框架与模型设计[J]. 测绘学报, 2022, 51(4):475-487.DOI:10.11947/j.AGCS.2022.20220027.
	GONG Jianya, ZHANG Mi, HU Xiangyun, et al. The design of deep learning framework and model for intelligent remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4):475-487.DOI:10.11947/j.AGCS.2022.20220027.
[9]	张永军, 万一, 史文中, 等. 多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J]. 测绘学报, 2021, 50(8):1068-1083.DOI:10.11947/j.AGCS.2021.20210079.
	ZHANG Yongjun, WAN Yi, SHI Wenzhong, et al. Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1068-1083.DOI:10.11947/j.AGCS.2021.20210079.
[10]	陶超, 阴紫薇, 朱庆, 等. 遥感影像智能解译：从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8):1122-1134.DOI:10.11947/j.AGCS.2021.20210089.
	TAO Chao, YIN Ziwei, ZHU Qing, et al. Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134.DOI:10.11947/j.AGCS.2021.20210089.
[11]	CHEN Sizhe, WANG Haipeng. SAR target recognition based on deep learning[C]//Proceedings of 2014 International Conference on Data Science and Advanced Analytics. Shanghai: IEEE, 2014: 541-547.
[12]	PEI Jifang, HUANG Yulin, HUO Weibo, et al. SAR automatic target recognition based on multiview deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4):2196-2210.
[13]	ZHU Xiaoxiang, MONTAZERI S, ALI M, et al. Deep learning meets SAR: concepts, models, pitfalls, and perspectives[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(4):143-172.
[14]	XIE Huiming, WANG Shuang, LIU Kun, et al. Multilayer feature learning for polarimetric synthetic radar data classification[C]//Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium. Quebec City: IEEE, 2014: 2818-2821.
[15]	GONG Maoguo, ZHAO Jiaojiao, LIU Jia, et al. Change detection in synthetic aperture radar images based on deep neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(1):125-138.
[16]	QIAN Kun, WANG Yuanyuan, SHI Yilei, et al. γ-Net: superresolving SAR tomographic inversion via deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-16.
[17]	WANG Teng, ZHANG Qi, WU Zhipeng. A deep-learning-facilitated, detection-first strategy for operationally monitoring localized deformation with large-scale InSAR[J]. Remote Sensing, 2023, 15(9):2310.
[18]	MUKHERJEE S, ZIMMER A, KOTTAYIL N K, et al. CNN-based InSAR denoising and coherence metric[C]//Proceedings of 2018 IEEE SENSORS. New Delhi: IEEE, 2018: 808-811.
[19]	SICA F, GOBBI G, RIZZOLI P, et al. Φ-Net: deep residual learning for InSAR parameters estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5):3917-3941.
[20]	SPOORTHI G E, SAI SUBRAHMANYAM GORTHI R K, GORTHI S. PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach[J]. IEEE Transactions on Image Processing, 2020, 29:4862-4872.
[21]	WU Zhipeng, WANG Teng, WANG Yingjie, et al. Deep learning for the detection and phase unwrapping of mining-induced deformation in large-scale interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:3121907.
[22]	HU Jun, WU Wenqing, GUI Rong, et al. Deep learning-based homogeneous pixel selection for multitemporal SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:3203975.
[23]	TIWARI A, NARAYAN A B, DIKSHIT O. Deep learning networks for selection of measurement pixels in multi-temporal SAR interferometric processing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166:169-182.
[24]	CHEN Yuxing, BRUZZONE L, JIANG Liming, et al. ARU-Net: reduction of atmospheric phase screen in SAR interferometry using attention-based deep residual U-Net[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7):5780-5793.
[25]	ZHAO Zhuoyi, WU Zherong, ZHENG Yi, et al. Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 180:227-237.
[26]	ANANTRASIRICHAI N, BIGGS J, ALBINO F, et al. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets[J]. Remote Sensing of Environment, 2019, 230:111179.
[27]	ROUET-LEDUC B, JOLIVET R, DALAISON M, et al. Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning[J]. Nature Communications, 2021, 12:6480.
[28]	LATTARI F, RUCCI A, MATTEUCCI M. A deep learning approach for change points detection in InSAR time series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:3155969.
[29]	MA Peifeng, ZHANG Fan, LIN Hui. Prediction of InSAR time-series deformation using deep convolutional neural networks[J]. Remote Sensing Letters, 2020, 11(2):137-145.
[30]	BÜURGMANN R, ROSEN P A, FIELDING E J. Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation[J]. Annual Review of Earth and Planetary Sciences, 2000, 28:169-209.
[31]	程惠红, 姚宜斌, 赵倩, 等. 从国家自然科学基金项目资助探讨大地测量学的学科架构与发展[J]. 测绘学报. 2023, 52(4):523-535. DOI:10.11947/j.AGCS.2023.20220663.
	CHENG Huihong, YAO Yibin, ZHAO Qian, et al. Exploring the geodesy principle architecture and development from the perspective of granted NSFC projects[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4):523-535. DOI:10.11947/j.AGCS.2023.20220663.
[32]	朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10):1717-1733. DOI:10.11947/j.AGCS.2017.20170350.
	ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1717-1733. DOI:10.11947/j.AGCS.2017.20170350.
[33]	FERRETTI A, PRATI C, ROCCA F. Permanent scatterers in SAR interferometry[C]//Proceedings of 1999 International Geoscience and Remote Sensing Symposium. Hamburg: IEEE, 1999.
[34]	BERARDINO P, FORNARO G, LANARI R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11):2375-2383.
[35]	FERRETTI A, FUMAGALLI A, NOVALI F, et al. A new algorithm for processing interferometric data-stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9):3460-3470.
[36]	ANSARI H, DE ZAN F, BAMLER R. Sequential estimator: toward efficient InSAR time series analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10):5637-5652.
[37]	RAMBOUR C, BUDILLON A, JOHNSY A C, et al. From interferometric to tomographic SAR: a review of synthetic aperture radar tomography-processing techniques for scatterer unmixing in urban areas[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(2):6-29.

智能化InSAR数据处理研究进展、挑战与展望

A review of intelligent InSAR data processing: recent advancements, challenges and prospects

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