Ground surface displacement measurement from SAR imagery using deep learning

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-12-21 DOI:10.1016/j.rse.2024.114577
Jinwoo Kim, Hyung-Sup Jung, Zhong Lu
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引用次数: 0

Abstract

Offset tracking using synthetic aperture radar (SAR) amplitude imagery is a valuable technique for detecting large ground displacements. However, the traditional offset tracking methods with the SAR datasets are computationally intensive and require significant time for processing. We have developed a novel cross-connection Siamese ResNet (CC-ResSiamNet). The model leverages multi-kernel offset tracking for preprocessing, followed by deep learning architectures that incorporate U-Net, cross-connections, and residual and attention blocks to predict pixel offsets between two SAR amplitude images. It is trained and tested on 200 K pairs of reference and secondary SAR amplitude images, alongside corresponding target offset data from Alaska's glaciers. The comparative analysis with multiple deep learning models confirmed that our designed model is highly generalizable, achieving rapid convergence, minimal overfitting, and high prediction accuracy. Through multi-scenario inference with glacier movements, earthquakes, and volcanic eruptions worldwide, the model demonstrates strong performance, closely matching the accuracy of traditional methods while offering significantly faster processing times through parallel computing. The model's rapid displacement mapping capability shows particular promise for improving disaster response and near real-time surface monitoring. While the approach encounters challenges in accurately capturing small-scale displacements, it opens new possibilities for SAR-based surface displacement prediction using machine learning. This research highlights the advantages of combining deep learning with SAR imagery for advancing geophysical analysis, with future applications anticipated as more commercial and scientific SAR missions launch globally.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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