InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances
| Autor: | Markus Even, Karsten Schulz |
|---|---|
| Přispěvatelé: | Publica |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Backscatter Computer science 0211 other engineering and technologies Persistent Scatterer distributed scatterer 02 engineering and technology Deformation (meteorology) 01 natural sciences InSAR Interferometric synthetic aperture radar ddc:550 Coherence (signal processing) Distributed Scatterer preprocessing persistent scatterer 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Pixel Estimation theory deformation Covariance coherence Earth sciences adaptive neighborhood covariance General Earth and Planetary Sciences |
| Zdroj: | Remote sensing, 10 (5), 744 |
| ISSN: | 2072-4292 |
| Popis: | Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used. |
| Databáze: | OpenAIRE |
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