A novel band selection method for hyperspectral data analysis

This paper proposes an innovative band selection (BS) method called prototype space band selection (PSBS) based only on class spectra. The main novelty of the proposed BS lies in band representation in a new space called prototype space, where bands are characterized in terms of class reflectivity to pose reflection properties of classes to bands. Having clustered the bands by K-means in the prototype space, highly correlated bands are trapped in a cluster. In each cluster a band that is close to cluster center identified as representative of clustered bands. In contrast to the previous BS methods, PSBS substitutes the search strategies with K-means clustering to find relevant bands. Moreover, instead of optimizing separability criteria, the accuracy of classification over a subset of training data is used to decide which band subset yield maximum accuracy. Experimental results demonstrated higher overall accuracy of PSBS compared to its conventional counterparts with limited sample sizes.