Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection

Authors:
Changjing Shang;Dave Barnes;Qiang Shen
Affiliations:
(Correspd. E-mail: cns@aber.ac.uk) Department of Computer Science, Aberystwyth University, Wales, UK;Department of Computer Science, Aberystwyth University, Wales, UK;Department of Computer Science, Aberystwyth University, Wales, UK
Venue:
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Year:
2011

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Abstract

This paper presents an application study of exploiting fuzzy-rough feature selection (FRFS) techniques in aid of efficient and accurate Mars terrain image classification. The employment of FRFS allows the induction of low-dimensionality feature sets from sample descriptions of feature vectors of a much higher dimensionality. Supported with comparative studies, the work demonstrates that FRFS helps to enhance both the effectiveness and the efficiency of conventional classification systems such as multi-layer perceptrons and K-nearest neighbors, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions.