Share Email Print
cover

Proceedings Paper

Dimensionality reduction of hyperspectral imagery based on spectral analysis of homogeneous segments: distortion measurements and classification scores
Author(s): Luciano Alparone; Fabrizio Argenti; Michele Dionisio; Leonardo Santurri
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this work, a new strategy for the analysis of hyperspectral image data is described and assessed. Firstly, the image is segmented into areas based on a spatial homogeneity criterion of pixel spectra. Then, a reduced data set (RDS) is produced by applying the projection pursuit (PP) algorithm to each of the segments in which the original hyperspectral image has been partitioned. Few significant spectral pixels are extracted from each segment. This operation allows the size of the data set to be dramatically reduced; nevertheless, most of the spectral information relative to the whole image is retained by RDS. In fact, RDS constitutes a good approximation of the most representative elements that would be found for the whole image, as the spectral features of RDS are very similar to the features of the original hyperspectral data. Therefore, the elements of a basis, either orthogonal or nonorthogonal, that best represents RDS, are searched for. Algorithms that can be used for this task are principal component analysis (PCA), independent component analysis (ICA), PP, or matching pursuit (MP). Once the basis has been calculated from RDS, the whole hyperspectral data set is decomposed on such a basis to yield a sequence of components, or features, whose (statistical) significance decreases with the index. Hence, minor components may be discarded without compromising the results of application tasks. Experiments carried out on AVIRIS data, whose ground truth was available, show that PCA based on RDS, even if suboptimal in the MMSE sense with respect to standard PCA, increases the separability of thematic classes, which is favored when pixel vectors in the transformed domain are homogeneously spread around their class centers.

Paper Details

Date Published: 5 February 2004
PDF: 8 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.514250
Show Author Affiliations
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Fabrizio Argenti, Univ. degli Studi di Firenze (Italy)
Michele Dionisio, Univ. degli Studi di Firenze (Italy)
Leonardo Santurri, Institute of Applied Physics Nello Carrara, CNR (Italy)


Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top