Presentation + Paper
18 October 2016 A novel framework for change detection in bi-temporal polarimetric SAR images
Author Affiliations +
Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100040Z (2016) https://doi.org/10.1117/12.2241636
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom
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.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Davide Pirrone, Francesca Bovolo, and Lorenzo Bruzzone "A novel framework for change detection in bi-temporal polarimetric SAR images", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040Z (18 October 2016); https://doi.org/10.1117/12.2241636
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CITATIONS
Cited by 5 scholarly publications.
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KEYWORDS
Polarimetry

Synthetic aperture radar

Expectation maximization algorithms

Binary data

Statistical analysis

Sensors

Critical dimension metrology

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