A Contrario Comparison of Local Descriptors for Change Detection in Very High Spatial Resolution Satellite Images of Urban Areas

Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high-resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation, and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Second, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an a contrario framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles, and illumination conditions.