Extraction and analysis of radar scatterer attributes for PAZ SAR by combining time series InSAR, PolSAR, and land use measurements

Ling Chang*, A. Kulshrestha, Bin Zhang, Xu Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
34 Downloads (Pure)

Abstract

Extracting meaningful attributes of radar scatterers from SAR images, PAZ in our case, facilitates a better understanding of SAR data and physical interpretation of deformation processes. The attribute categories and attribute extraction method are not yet thoroughly investigated. Therefore, this study recognizes three attribute categories: geometric, physical, and land-use attributes, and aims to design a new scheme to extract these attributes of every coherent radar scatterer. Specifically, we propose to obtain geometric information and its dynamics over time of the radar scatterers using time series InSAR (interferometric SAR) techniques, with SAR images in HH and VV separately. As all InSAR observations are relative in time and space, we convert the radar scatterers in HH and VV to a common reference system by applying a spatial reference alignment method. Regarding the physical attributes of the radar scatterers, we first employ a Random Forest classification method to categorize scatterers in terms of scattering mechanisms (including surface, low-, high-volume, and double bounce scattering), and then assign the scattering mechanism to every radar scatterer. We propose using a land-use product (i.e., TOP10NL data for our case) to create reliable labeled samples for training and validation. In addition, the radar scatterers can inherit land-use attributes from the TOP10NL data. We demonstrate this new scheme with 30 Spanish PAZ SAR images in HH and VV acquired between 2019 and 2021, covering an area in the province of Friesland, the Netherlands, and analyze the extracted attributes for data and deformation interpretation.

Original languageEnglish
Article number1571
JournalRemote sensing
Volume15
Issue number6
DOIs
Publication statusPublished - Mar 2023

Keywords

  • classification
  • deformation time series
  • PAZ SAR
  • random forest
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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