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

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.