A Geometrical Wavelet Framework for the Time-Series Analysis of Full-Polarimetric Features

Davide Pirrone , Abdourrahmane Mahamane Atto (1) , Emmanuel Trouve

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance

Polarimetric SAR (PolSAR) image time series have been employed for the analysis of temporal patterns of natural features in terms of the extended polarimetric scattering properties. However, the time series provide a rich scattering information that can be used for tracking and analyzing the evolution of targets, individuating smooth and/or abrupt changes. In this work we propose a wavelet framework that exploits the information from polarimetric features and analyze them to both mitigate the speckle effect on the multi-temporal information and improve the targets homogeneity using the multi-temporal information. The framework combines the powerful description from the main polarimetric decomposition features and the temporal analysis using geometrical wavelet transform. The analysis is applied on a multi-temporal polarimetric dataset of Radarsat-2 images acquired over the Argentière glacier site.

Mots clés: Polarimetric SAR; Time series; Incoherent Polarimetric Decomposition; Wavelet Temporal Operator; Multitemporal Analysis; Geometrical wavelet; Polarimetric SAR; Multi-temporal Analysis

Domaines: Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]; Recherche d'information [cs.IR]; Analyse numérique [cs.NA]; Modélisation et simulation; Théorie de l'information et codage [math.IT]; Statistiques [math.ST]; Théorie spectrale [math.SP]; Analyse fonctionnelle [math.FA]

Davide Pirrone, Abdourrahmane Mahamane Atto, Emmanuel Trouve. A Geometrical Wavelet Framework for the Time-Series Analysis of Full-Polarimetric Features. 2020 IEEE Radar Conference (RadarConf20), Sep 2020, Florence, Italy. pp.1-6, ⟨10.1109/RadarConf2043947.2020.9266403⟩. ⟨hal-03047702⟩