Unwrapping SAR interferograms with localized subsidence signal using deep neural network
Conference: EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar
03/29/2021 - 04/01/2021 at online
Proceedings: EUSAR 2021
Pages: 5Language: englishTyp: PDF
Authors:
Wu, Zhipeng (Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China & School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China)
Wang, Teng (School of Earth and Space Sciences, Peking University, Beijing, China)
Wang, Robert (Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China)
Abstract:
Phase unwrapping is an indispensable processing step of InSAR. However, conventional methods often underestimate the deformation due to severe noise and/or dense fringes. Here, we develop a new deep neural network to unwrap interferograms with localized subsidence signal. We train the network using synthetic interferograms with two-dimensional Gaussian shape subsidence and complex Gaussian noises, and apply the network to real interferograms with localized mining subsidence. The proposed method outperforms the standard methods by 76.3% on synthetic interferograms and ~50-times faster on real interferograms. The promising result shows the potential for rapid monitoring and quantification local deformation distributed in large area.