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.
Original language | English |
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Pages (from-to) | 3-13 |
Number of pages | 11 |
Journal | International Journal of Hybrid Intelligent Systems |
Volume | 8 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2011 |