Affiliations: Department of Computer Science, Aberystwyth University, Wales, UK | [v] Cairo University, Egypt | [w] Kyushu Institute of Technology, Japan | [x] University of Warsaw & Infobright Inc., Poland | [y] University of Calcutta, India | [z] UESTC, Chengdu, China
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.
Keywords: Mars images, image classification, fuzzy-rough feature selection