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Change detection is one of the most challenging issues when analyzingremotely sensed images. Comparing several multi-date images acquired throughthe same kind of sensor is the most common scenario. Conversely, designingrobust, flexible and scalable algorithms for change detection becomes even morechallenging when the images have been acquired by two different kinds ofsensors. This situation arises in case of emergency under critical constraints.This paper presents, to the best of authors' knowledge, the first strategy todeal with optical images characterized by dissimilar spatial and spectralresolutions. Typical considered scenarios include change detection betweenpanchromatic or multispectral and hyperspectral images. The proposed strategyconsists of a 3-step procedure: i) inferring a high spatial and spectralresolution image by fusion of the two observed images characterized one by alow spatial resolution and the other by a low spectral resolution, ii)predicting two images with respectively the same spatial and spectralresolutions as the observed images by degradation of the fused one and iii)implementing a decision rule to each pair of observed and predicted imagescharacterized by the same spatial and spectral resolutions to identify changes.The performance of the proposed framework is evaluated on real images withsimulated realistic changes.

Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach