Unsupervised change detection in built-up areas by multi-temporal polarimetric SAR images

Change detection in large urban areas is an application with increasing relevance. In this domain, Polarimetric SAR (PolSAR) sensors are receiving more attention recently. The enhanced polarimetric information provides useful features which can describe multi-temporal changes. In this work, we aim at introducing an approach for unsupervised change detection with focus on built-up areas that relies on the polarimetric information. This approach is based on the analysis of the multi-temporal α feature obtained from the Cloude-Pottier eigenvalue/eigenvector decomposition. Large differences in the α values can be associated to changes in the dominant scattering mechanism. These are likely to be associated to buildings when built-up areas are considered. Changes are detected according to an automatic and unsupervised approach. Validation is conducted on a pair of UAVSAR images acquired over Los Angeles, USA. Preliminary results highlight the effectiveness of proposed approach.