Lithological mapping from hyperspectral imagery using extended one-class kernel sparse representation

This paper presented a new method of lithological mapping using extended one-class kernel sparse representation, a new one-class classifier. In the proposed method, to address spectral variability of lithological types, learning vector quantization for novelty detection was adopted to produce several clusters before the classification process. The one-class kernel sparse representation was adopted to classify each obtained cluster. The proposed method was evaluated and validated in lithological mapping using EO-1 Hyperion hyperspectral data over an arid region in Xinjiang area, China.