A Novel Endmember Extraction Method Based on Manifold Dimensionality Reduction

Abstract

Because of multiple reflection and scattering, the mixed pixels in hyperspectral images are actually nonlinear spectral mixing. Traditional endmember extraction algorithm is based on linear spectral mixing model, so the extraction accuracy is not high. Aiming at the nonlinear structure of hyperspectral images, a novel endmember extraction method for hyperspectral images based on Euclidean distance and nonlinear dimensionality reduction is proposed. This method introduces Euclidean distance of image into the nonlinear dimensionality reduction algorithm of local tangent space permutation to remove redundant spatial information and spectral dimensional information in hyperspectral data and then extracts the endmembers from the reduced data by searching for the maximum volume of the simplex. Experiments on real hyperspectral data show that the proposed method has a good effect on hyperspectral image endmember extraction, and its performance is better than that of linear dimensionality reduction PCA and original LTSA algorithm.