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.
|Nifer y tudalennau||11|
|Cyfnodolyn||International Journal of Hybrid Intelligent Systems|
|Dynodwyr Gwrthrych Digidol (DOIs)|
|Statws||Cyhoeddwyd - 2011|