Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization

Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands. Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image analysis. In this paper, we propose a new band selection algorithm using sparse nonnegative matrix factorization (sparse NMF). Though acting as a clustering method for band selection, sparse NMF need not consider the distance metric between different spectral bands, which is often the key step for most common clustering-based band selection methods. By imposing sparsity on the coefficient matrix, the bands’ clustering assignments can be easily indicated through the largest entry in each column of the matrix. Experimental results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification.