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3 November 2005 A rough sets approach of hyperspectral image classification
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Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 604310 (2005)
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Rough set theory has a powerful capability for attributes reduction and classification rules extraction, while artificial neural network (ANN) performances well in classification problems with a satisfactory accuracy. In this paper we focus our attention to investigate a way of integrating rough set theory and multi layer perceptron (MLP) in soft computing paradigm for classification and rule generation of hyperspectral remote sensing image classification. The novelty of this method lies in applying rough set theory for extracting classification rules and computing fuzzy membership values directly from decision table after attributes reduction on a real-valued attribute table consisting of classification features. The successful application of this approach in hyperspectral remote sensing images mineral classification illustrates the flexibility and practicality of this new approach.
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Zhaocong Wu and Deren Li "A rough sets approach of hyperspectral image classification", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604310 (3 November 2005);

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