Cloud Model Based SaDE for Feature Selection in Hyper-spectral Image Data

Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. In this paper, attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Cloud model based self-adaptive differential evolution (CMSaDE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on KSC data set and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.