Nonlinear unmixing methods for hyperspectral image (HSI) have attracted increasing interests since they can overcome the inherent limitations of the linear ones. In this paper, we regard the spectral unmixing as a blind source separation problem in the feature space, and develop a novel kernel based nonnegative matrix factorization method to estimate the endmembers and the abundances simultaneously. Moreover, the abundance sparsity property of HSI data in the feature space is exploited to promote more accurate unmixing results. Experiments are conducted on real HSI data with intimate mixture of minerals and the unmixing performance is compared with the state-of-the-art methods to demonstrate the effectiveness of the proposed method.
['Wenhong Wang', 'Yuntao Qian']
Kernel based sparse NMF algorithm for hyperspectral unmixing