FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES
Change detection is one of the most important applications of Polarimetric Synthetic Aperture Radar (PolSAR) data in monitoring urban development and supporting urban planning due to the sensibility of SAR signal to geometrical and physical properties of terrestrial features. In this paper, we proposed an unsupervised change detection method using change indices extracted from PolSAR images. Kernel k-means clustering was then performed to extract changed areas. The kernel k-means clustering is an unsupervised algorithm that maps the input features to higher Hilbert dimension space by using a kernel function. To better representation of changed areas, different change indices were generated. The method was applied to UAVSAR L-band SAR images acquired over an urban area in San Andreas, United States. We evaluated the change detection performance based on kappa and overall accuracies of the proposed approach and compared with other well-known classic methods.