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

David Preston Barnes, Changjing Shang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

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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 languageEnglish
Pages (from-to)3-13
Number of pages11
JournalInternational Journal of Hybrid Intelligent Systems
Issue number1
Publication statusPublished - 2011


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