Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Liming Pu,Liang Li,Xiaoling Zhang,Jun Shi,Liming Zhou,Shunjun Wei

doi:10.3390/rs14051174

Liming Pu, Liang Li + Show 4 more

Open Access

https://doi.org/10.3390/rs14051174

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Abstract

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.

Highlights

Accepted: 25 February 2022Interferometric synthetic aperture radar (InSAR) is becoming increasingly important in the field of remote sensing and has been successfully applied in topography mapping and deformation monitoring [1–5]
Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-phase-filtering network (PFNet) can predict accurate filtered phase from a large number of interferometric phase images with different noise levels. Experiments both on simulated and real InSAR data show that the proposed method significantly outperforms the three conventional well-established methods and a deep learning-based method
NL-PFNet is designed based on the encoder–decoder structure and nonlocal feature selection strategy

Summary

Introduction

Accepted: 25 February 2022Interferometric synthetic aperture radar (InSAR) is becoming increasingly important in the field of remote sensing and has been successfully applied in topography mapping and deformation monitoring [1–5]. In the InSAR data processing pipeline, the interferometric phase is formed by two or more SAR complex images acquired at different viewing angles or at different times. The basic idea of most spatial domain and transform domain methods is to filter out noise through the window processing of local neighboring pixels in the image spatial domain or transform domain, such as the well-established Lee filter [7] and Goldstein filter [10]. In these two types of methods, the inherent nonlocal (NL) self-similarity of interferometric phase images has Published: 27 February 2022

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Discussion

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