Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.47
Alexandre Blansche , Louis Pasteur University, Strasbourg, France
Annett Wania , Louis Pasteur University, Strasbourg, France
Pierre Gan?arski , Louis Pasteur University, Strasbourg, France
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.
A. Blansche, A. Wania and P. Gan?arski, "Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 231-236.