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Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling

Abstract : This paper proposes a framework to integrate spatial information into unsupervised feature extraction for hyperspectral images. In this approach a nonlinear scale-space representation using morphological levelings is formulated. In order to apply feature extraction, tensor principal components are computed involving spatial and spectral information. The proposed method has shown significant gain over the conventional schemes used with real hyperspectral images. In addition, the proposed framework opens a wide field for future developments in which spatial information can be easily integrated into the feature extraction stage. Examples using real hyperspectral images with high spatial resolution showed excellent performance even with a low number of training samples.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00458687
Contributor : Santiago Velasco-Forero <>
Submitted on : Monday, February 22, 2010 - 10:21:16 AM
Last modification on : Thursday, September 24, 2020 - 4:38:04 PM
Long-term archiving on: : Friday, June 18, 2010 - 9:35:41 PM

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Santiago Velasco-Forero, Jesus Angulo. Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing:, Aug 2009, Grenoble, France. pp.1 - 4, ⟨10.1109/WHISPERS.2009.5289059⟩. ⟨hal-00458687⟩

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