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Proceedings Paper

Unsupervised component reduction of hyperspectral images and clustering without performance loss: application to marine algae identification
Author(s): B. Chen; K. Chehdi; E. De Oliveira; C. Cariou; B. Charbonnier
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Paper Abstract

We propose in this communication a classification method adapted to the grouping of spectral components which provide a similar information. After this step, a single band is automatically selected for every band class in order to cluster the pixels of the images. This method is completely unsupervised.

The proposed reduction approach is deterministic and iterative. It includes a connectivity criterion between bands which uses the Manhattan distance. This criterion allows the automatic partitioning of M spectral bands, leading to an identification of the most relevant spectral bands to keep in the further pixel classification process. Moreover, the use of this criterion avoids classes with only one band. The spectral band selected to represent a given class is the closest to all the other bands of this class, with respect to the used metric.

The spectral bands reduction developed has been evaluated and validated with our unsupervised descending hierarchical classification pixel method (UDHC), with the addition of a regularization step. A real hyperspectral image composed of 100 spectral bands has been used for the experimental study.

Paper Details

Date Published: 18 October 2016
PDF: 14 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040Q (18 October 2016); doi: 10.1117/12.2241190
Show Author Affiliations
B. Chen, IETR, CNRS, Univ. de Rennes 1 (France)
EDF Recherche et Développement (France)
K. Chehdi, IETR, CNRS, Univ. de Rennes 1 (France)
E. De Oliveira, EDF Recherche et Développement (France)
C. Cariou, IETR, CNRS, Univ. de Rennes 1 (France)
B. Charbonnier, EDF Recherche et Développement (France)


Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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