A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

ABSTRACT

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.

KEYWORDS:

• Interferometric synthetic aperture radar
• distributed target
• homogeneous pixel selection
• deformation monitoring
• time series analysis

Data availability statement

The MATLAB codes for Monte Carlo simulation experiment, Phase optimization and BWS-DIE algorithm are available at https://github.com/Ming-subsidence/time-series-deformation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the [Natural Science Foundation of China] under Grant [number 41907240]; [Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People’s Republic of China] under Grant [number KLSMNR-202203]; [the China Postdoctoral Science Foundation] under Grant [number 2019M663601];[A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions]; [Key Research and Development Program of Xuzhou] under Grant [number KC20180]; [the Shaanxi Province Science and Technology Innovation team] under Grant [number 2021TD-51]; [the Fundamental Research Funds for the Central Universities, CHD] under Grant [numbers 300102260301 and 300102261108]; [the European Space Agency through the ESA-MOST DRAGON-5 project] under Grant [number 59339]; and [International Cooperation and Exchanges National Natural Science Foundation of China] under Grant [number 41920104010].