This paper presents a novel application of fuzzy-rough set-based feature selection (FRFS) for Mars terrain image classification. The work allows the induction of low-dimensionality feature sets from sample descriptions of feature patterns of a much higher dimensionality. In particular, FRFS is applied in conjunction with multi-layer perceptron and K-nearest neighbor based classifiers. Supported with comparative studies, the paper demonstrates that FRFS helps to enhance the effectiveness and efficiency of conventional classification systems, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Shang, C., Barnes, D., & Shen, Q. (2009). Taking fuzzy-rough application to mars fuzzy-rough feature selection for mars terrain image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5908 LNAI, pp. 209–216). https://doi.org/10.1007/978-3-642-10646-0_25
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