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.
Online Multi-resolution Fusion of Space-borne Multispectral Images
05.03.2022
4098222 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Subjective Assessment of Multispectral Fusion Images
British Library Conference Proceedings | 2014
|Multispectral image data fusion using POCS and super-resolution
British Library Online Contents | 2006
|Supervised Fusion - Classification of Multispectral Images Using Fuzzy Sets Theory
British Library Conference Proceedings | 1999
|Multispectral intelligent fusion techniques
British Library Online Contents | 1992
|Morphological partitioning of multispectral images
British Library Online Contents | 1996
|