Online fusion of multi-resolution multispectral images with weakly supervised temporal dynamics
نویسندگان
چکیده
Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint is trade-off between spatial/spectral resolution their revisiting time, consequence design physical constraints imposed by orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with spatial resolutions used. We leverage relationship at multiple modalities to generate high-resolution image sequences higher rates. To achieve goal, formulate fusion method recursive state estimation problem study its performance filtering smoothing contexts. Furthermore, calibration strategy proposed estimate time-varying temporal dynamics sequence using only small amount historical data. Differently training process traditional machine learning algorithms, which usually require large datasets computation times, parameters dynamical model are calibrated based an analytical expression that uses two dataset. A distributed version Bayesian strategies also reduce computational complexity. evaluate methodology consider water mapping task real Landsat MODIS fused generating high spatial–temporal estimates. Our experiments show outperforms competing methods both accuracy tasks.
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ژورنال
عنوان ژورنال: Isprs Journal of Photogrammetry and Remote Sensing
سال: 2023
ISSN: ['0924-2716', '1872-8235']
DOI: https://doi.org/10.1016/j.isprsjprs.2023.01.012