Conditional copula for change detection on heterogeneous SAR data

A new preprocessing technique is presented in this paper to perform automatic change detection in multitemporal multimodal remotely sensed images, mainly synthetic aperture radar (SAR) ones. This technique is dedicated to the case where the two acquisitions, before and after an major disaster, are different for some reason (different sensor, modality of acquisition or climatic conditions). A measure, based on the local statistics of the images between the two dates, has proved to be a relevant change indicator. Nevertheless, the measure is valid when the two observations have been acquired with a similar point of view only. When the modalities of acquisition differ, local statistics tend to be too different, from one image to the other, to be relevant to the ground evolution without mixing to the normal changes. The technique, that overcomes this constraint, is based on the assumption that some dependence exists indeed between the two images. This dependence is modelled by the copula theory and used to perform an estimation of the local statistics that would have been observed if the modality of the first image had been similar to the other. It yields an estimation of local statistics of the first image, through the point of view of the latter. Then, usual comparison of those statistics may be applied to perform change detection. Some results are shown on a pair of ERS images and pairs of SPOT/ERS acquired before and after a flood.