Change detection between multi-band images using a robust fusion-based approach

This paper proposes a robust fusion-based strategy to detect changes between two multi-band optical images with different spatial and spectral resolutions, e.g., a multispectral high spatial resolution image and a hyperspectral low spatial resolution image. The dissimilarity between sensor resolutions makes the change detection problem challenging, which has been generally bypassed in the literature: most often, the two images are crudely and independently resampled in order to get the same spatial and spectral resolutions and finally, classical change detection methods are applied. However, the resampling operation tends to lose information. In this paper, we propose a method that more effectively uses the available information: the two observed images are respectively modeled as spatial and spectral degradations of two latent images characterized by the same high spatial and high spectral resolutions. Representing the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Change detection is then envisioned through the solution of an inverse problem, shown to be a specific instance of multi-band image fusion. The proposed method is applied to real images with simulated realistic changes. A comparison with state-of-the-art change detection methods evidences the proposed method superiority.