Integration of Spatial-Spectral Information Based Endmember Extraction for Hyperspectral Image

In the present paper, a novel algorithm is proposed to integrate both spatial and spectral information for automatic EE (Integration of spatial-spectral information based endmember extraction, ISEE). At first, the image is divided into some subspaces for improvement of spectral contrast. Then, the subset of the image is projected to the feature space related to the image endmembers, and the candidate endmember spectra are extracted through orthogonal subspace projection analysis. At last, the endmember spectra are refined under the constraint of image spatial context and spectral information. The performances of different endmember extraction methods are compared using both synthetic hyperspectral image and real hyperspectral image. The experimental results demonstrate that ISEE incorporated with spatial information is effective, and the endmember spectra extracted by ISEE are more accurate than by some common EE methods.