Comparison of MACLAW with several attribute selection methods for classification in hyperspectral images

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.