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ISPRS Int. J. Geo-Inf. 2016, 5(6), 83; doi:10.3390/ijgi5060083

Morphological Principal Component Analysis for Hyperspectral Image Analysis

MINES ParisTech, PSL-Research University, CMM-Centre de Morphologie Mathématique, 35 rue Saint-Honor7305 Fontainebleau, France
This paper is an extended version of our paper published in G. Franchi, J. Angulo, Comparative Study on Morphological Principal Component Analysis of Hyperspectral Images. In Proceedings of the 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS’14), Lausannne, Switzerland, 24–27 June 2014.
These authors contributed equally to this work.
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Academic Editors: Beatriz Marcotegui and Wolfgang Kainz
Received: 17 December 2015 / Revised: 10 May 2016 / Accepted: 11 May 2016 / Published: 3 June 2016
(This article belongs to the Special Issue Mathematical Morphology in Geoinformatics)

Abstract

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 proposed to add spatial information are discussed in this article. They are based on mathematical morphology operators. These morphological operators are the area opening/closing, granulometries and grey-scale distance function. We name the proposed family of techniques the Morphological Principal Component Analysis (MorphPCA). Present approaches provide new feature spaces able to jointly handle the spatial and spectral information of hyperspectral images. They are computationally simple since the key element is the computation of an empirical covariance matrix which integrates simultaneously both spatial and spectral information. The performance of the different feature spaces is assessed for different tasks in order to prove their practical interest. View Full-Text
Keywords: spatial machine learning; hyperspectral images; dimensionality reduction; mathematical morphology spatial machine learning; hyperspectral images; dimensionality reduction; mathematical morphology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Franchi, G.; Angulo, J. Morphological Principal Component Analysis for Hyperspectral Image Analysis. ISPRS Int. J. Geo-Inf. 2016, 5, 83.

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