Classification of hyperspectral images with very small training size using sparse unmixing

Hyperspectral images are high dimensional while the available number of training samples can be very low. For very small training sizes, classical supervised classification strategies may fail. In this work we propose an alternative, semi-supervised approach which is based on sparse unmixing. In this method, all training samples are gathered in a dictionary and serve as possible endmembers. Unmixing then reveals the relative contributions of the different training samples to an unlabeled sample. Since standard unmixing strategies as the Fully Constrained Linear Spectral Unmixing (FCLSU) typically assume only one endmember per class, we investigate the use of sparse unmixing. In this work, we apply the SunSAL algorithm. We show that this method outperforms SVM classification in the case of extremely small training sizes of only a few samples per class.