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.
In the recent years, new satellite SAR data with very-high spatial resolution are available for scientific studies. In the urban scenario, these data are of high interest. Because, they allow the detection of changes at fine resolution, such those affecting buildings. Thus, they represent a precious information for rescue activities. Here, we study and design a geometrical model for representing possible kinds of damages in buildings. Among the different kinds of damages, we focus the attention on the one associated to the façades visible from the SAR sensor. According to the model and by using a ray-tracing method (i.e., the electromagnetic propagation is approximated with optical rays), we develop an analytical model for the backscattering of partially damaged buildings and investigate their behaviors in multi-temporal VHR SAR images. Both surface and multiple-bounce contributions are considered and analyzed by varying geometrical parameters. The resulting single date and multi-temporal patterns are validated on Cosmo-SkyMed data acquired over L’Aquila before and after the seismic event that hit the city in March 2009.