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.
https://hal.archives-ouvertes.fr/hal-01256947
Contributeur : Gianni Franchi <>
Soumis le : vendredi 15 janvier 2016 - 15:27:57
Dernière modification le : mardi 12 septembre 2017 - 11:41:19
Document(s) archivé(s) le : samedi 16 avril 2016 - 10:46:13
Gianni Franchi, Jesus Angulo. Comparative study on morphological principal component analysis of hyperspectral images. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 6th Workshop on, Jun 2014, Lausanne, Switzerland. <hal-01256947>
