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Satellite imaging has a central role in monitoring, detecting and estimatingthe intensity of key natural phenomena. One important feature of satelliteimages is the trade-off between spatial/spectral resolution and theirrevisiting time, a consequence of design and physical constraints imposed bysatellite orbit among other technical limitations. In this paper, we focus onfusing multi-temporal, multi-spectral images where data acquired from differentinstruments with different spatial resolutions is used. We leverage the spatialrelationship between images at multiple modalities to generate high-resolutionimage sequences at higher revisiting rates. To achieve this goal, we formulatethe fusion method as a recursive state estimation problem and study itsperformance in filtering and smoothing contexts. The proposed strategy clearlyoutperforms competing methodologies, which is shown in the paper for real dataacquired by the Landsat and MODIS instruments.

Online multi-resolution fusion of space-borne multispectral images