Persistent scatterer densification through the application of capon- And APES-Based SAR reprocessing algorithms

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Abstract

Capon's minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained.

Original languageEnglish
Article number8718325
Pages (from-to)7521-7533
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number10
DOIs
Publication statusPublished - 2019

Keywords

  • Amplitude and phase estimation (APES)
  • Capon
  • persistent scatterer (PS) density
  • PS interferometry (PSI)
  • superresolution (SR)

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