Hyperspectral band selection using a collaborative sparse model

In our previous research, we have proposed band-similarity-based unsupervised band selection approaches, which are proven to be very efficient. In this paper, we propose to use a collaborative sparse model for further improvement. Specifically, the pre-selected bands using the fast method, called NFINDR+LP, are further refined using a collaborative sparse model. It not only requires that the linear regression coefficients are sparse, but also requires that the same set of active bands is shared by all the bands to be removed. With the collaborative sparseness constraint being relaxed, the final selected bands can be further improved, that is, the band subset with the same number of bands can provide better classification accuracy. Based on the preliminary result, the proposed sparse model is also capable of finding the minimum number of bands to be selected.