An endmember extraction algorithm for hyperspectral imagery based on kernel orthogonal subspace projection

Endmember extraction is a key step of spectral unmixing. In order to extract endmembers more precisely from nonlinear mixed hyperspcetral imagery, an unsupervised kernel-based orthogonal subspace projection (UKOSP) technique is proposed in this paper. Without considering the noise, the maximal pixel vector in the imagery would be regarded as an endmember, then was removed the effect of it by kernel orthogonal subspace projection method to get another orthogonal imagery. Experimental results of simulated and real data prove that the proposed UKOSP approach outperforms the linear endmember extraction algorithms such as vertex component analysis and unsupervised kernel-based orthogonal subspace projection.