Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images Hong Kong, China December 18-December 22 ISBN: 0-7695-2702-7
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.47
MACLAW is a clustering algorithm with local attribute weighting performed through cooperative coevolution. In this paper, we will compare the attributes weights obtained by MACLAW with several relevance indices for band selection on DAIS remotely sensed image which registers spectral object information in 79 bands of at least 2 nm. MACLAW capacities are also assessed by comparing its results to a supervised classification method for feature extraction proposed by the software ENVI (RSI Inc.). The MACLAW results are satisfying. Classification results are similar to the results of the supervised method. Supervised classification results are slightly improved using only a feature subset identified by MACLAW.
Citation:
Alexandre Blansche, Annett Wania, Pierre Gan?arski, "Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images," icdmw, pp.231-236, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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