OCLeaS - A tomographic PSI Algorithm using Orthogonal Matching Pursuit and Complex Least Squares

被引：3

作者：

Rebmeister, Matthieu ^{[1
]}

Schenk, Andreas ^{[1
]}

Bradley, Patrick Erik ^{[1
]}

Doerr, Nils ^{[1
]}

Hinz, Stefan ^{[1
]}

机构：

[1] Inst Photogrammetry & Remote Sensing, Engessersrasse 6, D-76131 Karlsruhe, Germany

来源：

INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS / INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT / INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES 2020 (CENTERIS/PROJMAN/HCIST 2020) | 2021年 / 181卷

关键词：

Persistent Scatterer Interferometry; TomoSAR; Compressive Sensing; Parameter Estimation;

D O I：

10.1016/j.procs.2021.01.143

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter describes an algorithm using Compressive Sensing to analyse a stack of SAR images to monitor small urban areas. It uses the combination of the Orthogonal Matching Pursuit and Complex Least Squares, in order to estimate the residual height, thermal dilation coefficient and the displacement rate associated with one or multiple dominant persistent scatterers in a SAR resolution cell. It can be interpreted as a hybrid tomographic-PSI algorithm. The proposed method is tested with simulated data over single and double PS. Then it is applied to a stack of 79 SAR scenes over the Central Business District of the city of Perth in Australia. The results are compared to a conventional algorithm. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：220 / 230

页数：11

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