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

doi:10.11947/j.AGCS.2024.20230440

Abstract

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

CLC Number:

P258

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.

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