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A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

Lookup NU author(s): Professor Zhenhong Li



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Distributed scatterer interferometric synthetic aperture radar (DS InSAR) technology has been widely used in various fields. Homogeneous pixel selection is a crucial step in the use of DS InSAR, directly affecting the estimation precision and reliability of subsequent parameter calculations. The existing algorithms for selecting homogeneous pixels have inherent limitations, such as requiring many heterogeneous samples and strict requirements surrounding the required number of synthetic aperture radar (SAR) images. To address these problems, a new sequential selection algorithm for homogeneous pixels is proposed, based on the Baumgartner–Weiss–Schindler (BWS) test algorithm and dynamic interval estimation (DIE) theory. According to Monte Carlo simulation experiments, the average standard deviation (STD) of the mean of the rejection of the BWS-DIE algorithm under six sample conditions is 0.014. Compared with three existing algorithms, including the Kolmogorov‒Smirnov (KS), BWS and fast statistically homogeneous pixel selection (FaSHPS) algorithms, the BWS-DIE algorithm improves homogeneous pixel selection precision by 64.3%, 69.4% and 25.3%, respectively. In the real data experiment, 12 scenes of Advanced Land Observing Satellite-1 Phased Array type L-band Synthetic Aperture Radar (ALOS-1 PALSAR) data from February 2007 to March 2011 were used and the BWS-DIE multitemporal InSAR (MT InSAR) method based on the BWS-DIE algorithm was applied to surface subsidence monitoring in the western mining area of Xuzhou, Jiangsu Province, China. The experimental results show that, compared with the Stanford Method for Persistent Scatterers (StaMPS), the BWS-DIE MT InSAR method improves the ability to monitor the maximum subsidence by 12.3%, increases the point density by 5.7 times and decreases the root mean square error (RMSE) by 50%. In addition, new surface deformation patterns are found in the spatial-temporal evolution. The above experimental results show that the proposed BWS-DIE algorithm exhibits remarkable advantages in selection power and selection precision and is not limited by the number of SAR images. The proposed algorithm can further broaden the application scenarios for DS InSAR and provide high-quality and reliable monitoring data for subsequent scientific research.

Publication metadata

Author(s): Chen B, Yang J, Li Z, Yu C, Yu Y, Qin L, Yang Y, Yu H

Publication type: Article

Publication status: Published

Journal: GIScience and Remote Sensing

Year: 2023

Volume: 60

Issue: 1

Online publication date: 17/06/2023

Acceptance date: 22/05/2023

Date deposited: 11/07/2023

ISSN (print): 1548-1603

ISSN (electronic): 1943-7226

Publisher: Taylor and Francis Ltd.


DOI: 10.1080/15481603.2023.2218261


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Funder referenceFunder name
China Postdoctoral Science Foundation
Fundamental Research Funds for the Central Universities
International Cooperation and Exchanges National Natural Science Foundation of China
Key Research and Development Program of Xuzhou
Natural Science Foundation of China
Ministry of Natural Resources of the People’s Republic of China
Priority Academic Program Development of Jiangsu Higher Education Institutions
Shaanxi Province Science and Technology Innovation team