Morphological Principal Component Analysis for Hyperspectral Image Analysis

Morphological Principal Component Analysis for Hyperspectral Image Analysis

This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches pro...

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Bibliographic Details
Main Authors: Gianni Franchi, Jesús Angulo
Format: Article
Language:English
Published: MDPI AG 2016-06-01
Series:ISPRS International Journal of Geo-Information
Subjects:
spatial machine learning
hyperspectral images
dimensionality reduction
mathematical morphology
Online Access:http://www.mdpi.com/2220-9964/5/6/83

Internet

http://www.mdpi.com/2220-9964/5/6/83

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