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Proceedings Paper

Robust automatic classification method for hyperspectral imagery
Author(s): Luis Gomez-Chova; Javier Calpe-Maravilla; Gustavo Camps-Valls; Jose D. Martin-Guerrero; Emilio Soria-Olivas; Joan Vila; Luis Alonso-Chorda; Jose Moreno
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Paper Abstract

In this paper, we propose a new approach to the classification of hyperspectral images. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. The method is based on the general formulation of the expectation-maximization (EM) algorithm. This method is applied to crop cover recognition of six hyperspectral images from the same area acquired with HyMap spectrometer during the DAISEX99 campaign. For classification purposes, six different classes are considered in this area: corn, wheat, sugar beet, barley, alfalfa, and soil. Classification accuracy results are compared to common methods: ISODATA, Learning Vector Quantization, Gaussian Maximum Likelihood, Expectation-Maximization, and Neural Networks. The good performance confirms the validity of the proposed approach in terms of accuracy and robustness.

Paper Details

Date Published: 5 February 2004
PDF: 10 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.510673
Show Author Affiliations
Luis Gomez-Chova, Univ. de Valencia (Spain)
Javier Calpe-Maravilla, Univ. de Valencia (Spain)
Gustavo Camps-Valls, Univ. de Valencia (Spain)
Jose D. Martin-Guerrero, Univ. de Valencia (Spain)
Emilio Soria-Olivas, Univ. de Valencia (Spain)
Joan Vila, Univ. de Valencia (Spain)
Luis Alonso-Chorda, Univ. de Valencia (Spain)
Jose Moreno, Univ. de Valencia (Spain)

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

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