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Comparative study on morphological principal component analysis of hyperspectral images
Abstract : This paper deals with a problem of reducing the dimension of hyperspectral images using the principal component analysis. Since hyperspectral images are always reduced before any process, we choose to do this reduction by adding spatial information that can be useful then for classification process; to do it we choose to project our data in new spaces thanks mathematical morphology.
Cited literature [10 references]
https://hal.archives-ouvertes.fr/hal-01256947
Contributor : Gianni Franchi Connect in order to contact the contributor
Submitted on : Friday, January 15, 2016 - 3:27:57 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM
Long-term archiving on: : Saturday, April 16, 2016 - 10:46:13 AM
Contributor : Gianni Franchi Connect in order to contact the contributor
Submitted on : Friday, January 15, 2016 - 3:27:57 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM
Long-term archiving on: : Saturday, April 16, 2016 - 10:46:13 AM
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WhispersFRANCHI_ANGULO.pdf
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