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

作　　者：江利明 邵益 周志伟 马培峰[3] 王腾 JIANG Liming;SHAO Yi;ZHOU Zhiwei;MA Peifeng;WANG Teng(State Key Laboratory of Geodesy and Earth s Dynamics,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences,Wuhan 430077,China;College of Earth and Planetary Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Space and Earth Information Science,The Chinese University of Hong Kong,Hong Kong 999077,China;School of Earth and Space Sciences,Peking University,Beijing 100871,China)

机构地区：[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,中国香港999077 [4]北京大学地球与空间科学学院,北京100871

出　　处：《测绘学报》2024年第6期1037-1056,共20页Acta Geodaetica et Cartographica Sinica

基　　金：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)。

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。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.

关 键 词：INSAR 地表形变 智能处理 深度学习 神经网络 

分 类 号：P258[天文地球—测绘科学与技术]