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Proceedings Paper

A novel framework for change detection in bi-temporal polarimetric SAR images
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Paper Abstract

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Paper Details

Date Published: 18 October 2016
PDF: 12 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040Z (18 October 2016); doi: 10.1117/12.2241636
Show Author Affiliations
Davide Pirrone, Univ. degli Studi di Trento (Italy)
Fondazione Bruno Kessler (Italy)
Francesca Bovolo, Fondazione Bruno Kessler (Italy)
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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