COSMO-SkyMed for Unsupervised Urban Change Detection using Radar Backscattering and Interferometric Coherence

In this paper a new approach based on the use of Synthetic Aperture Radar COSMO-SkyMed products to verify urban change detection and to observe new constructions is presented. SLC products information has been exploited, since the proposed procedure combines backscattering coefficient and coherence information as extracted from two interferometric data, acquired in a short time interval. The algorithm exploits the information from backscatter intensity and interferometric coherence. Firstly, the interferometric SAR couple is processed by an unsupervised Neural-Networks, particularly PCNN (Pulse Coupled Neural Network) is applied to create a preliminary changes map based on the difference of backscatter intensity information. Then, accuracy is further improved by the fusion with the coherence information. The achieved results shown as the combination of backscattering and coherence information, extracted from Very High Resolution SAR data, allows to provide very accurate urban change detections with a fast and unsupervised procedure. The latter is particularly suitable to process quickly huge amount of SAR data since its lower computational requirements with respect to e.g. supervised algorithms.