Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation

Université Toulouse III - Paul Sabatier · Institut National Polytechnique de Toulouse · +7 more institutions

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Abstract

This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients.…

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674
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74.05
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100%
References
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Authors

4

Topics & keywords

Keywords
  • Multispectral image
  • Hyperspectral imaging
  • Sparse approximation
  • Artificial intelligence
  • Regularization (linguistics)
  • Image fusion
  • Computer science
  • Subspace topology
UN Sustainable Development Goals
  • Quality Education
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