Urban Change Detection for High-Resolution Fully Polarimetric SAR Using a Modified Heterogeneous Clutter Model

Inhalt:
Based on the clutter statistical characteristics of fully polarimetric SAR (PolSAR) image, this paper presents a novel change detection method using a modified heterogeneous clutter model. Firstly, a multilook product model is introduced to describe the heterogeneous clutter in PolSAR data, and a new generalized likelihood ratio test (GLRT) distance is presented to measure the degree of evolution between the statistical characteristics of multi temporal full SAR images. Compared with spherically invariant random vectors (SIRV) distance, the stochastic texture description is considered in proposed GLRT distance. Afterwards, Kittler and Illingworth (K&I) minimum error threshold segmentation method is applied to extract the changed area.