Endmember extraction algorithm using orthogonal subspace projection and local spatial correlation

Abstract

Endmember extraction plays an important role in spectral unmixing. Traditional endmember extraction algorithms, such as EEAs, only use spectral information to get the endmember, but ignore the spatial characteristic of the remote sensing image. Because of this, the algorithms are susceptible to the noise and anomaly image, which reduces the accuracy of endmember extraction. Focusing on this problem, a new EEA (OSP-LSC) combining subspace projection and local spatial information is proposed. Based on the theory of convex simplex, the algorithm sequentially extracts the endmembers by combining the subspace projection and simplex volume analysis. During the extracting process, the local spectral similarity constraint is used to improve the robustness to the noise and anomaly pixel, which also avoids to the huge computational cost caused by global spatial information. Furthermore, the simplex volume calculation is free of dimensionality which may cause the possible loss of original information, The experimental results on synthetic and real hyperspectral image show that the experimental results on synthetic and real hyperspectral image show that the proposed algorithm can improve the accuracy of the endmember extraction, and is more robust to the noise and anomaly pixels compared to the spectral based EEAs.