A combination of k-means clustering and entropy filtering for band selection and classification in hyperspectral images

ABSTRACT

Hyperspectral images usually have large volumes of data comprising hundreds of spectral bands. Removal of redundant bands can both reduce computational time and improve classification performance. This work proposes and analyses a band-selection method based on the k-means clustering strategy combined with a classification approach using entropy filtering. Experimental results in different terrain images show that our method can significantly reduce the number of bands while maintaining an accurate classification.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are thankful to FAPESP [grant number #2011/22749-8] and CNPq [grant number #307113/2012-4] for their financial support.