A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images

In this context, a novel approach combining inverse Gaussian model (IGM) and the Kittler-Illingworth (KI) criterion has been proposed to carry out tunsupervised change detection in synthetic aperture radar (SAR) images. The minimum error threshold could be computed by exploiting the Bayes decision theory under the assumption that hybrid IGM could describe the distribution of the changed and unchanged class in difference image. Experiments carried out on two sets of multi-temporal SAR images indicate that the proposed approach can effectively estimate the probability density function of the unchanged and changed classes in the difference image and acquire a reasonable threshold for yielding a better change map from the difference image.