Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena. One important feature of satellite images is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. The proposed strategy clearly outperforms competing methodologies, which is shown in the paper for real data acquired by the Landsat and MODIS instruments.
@article{arxiv.2204.12566,
title = {Online multi-resolution fusion of space-borne multispectral images},
author = {Haoqing Li and Bhavia Duvviri and Ricardo Borsoi and Tales Imbiriba and Edward Beighley and Deniz Erdogmus and Pau Closas},
journal= {arXiv preprint arXiv:2204.12566},
year = {2022}
}