ENDMEMBER EXTRACTION FOR HYPERSPECTRAL IMAGE BASED ON INTEGRATION OF SPATIAL-SPECTRAL INFORMATION

Endmember extraction (EE) plays an extremely important role for hyperspectral mixture analysis, and many EE methods have been proposed in recent years. However, most approaches have been designed from a spectroscopic viewpoint and thus, tend to neglect the existing spatial correlation between pixels. In this paper, a novel algorithm is proposed to integrate both spatial and spectral information for automatic EE (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 performance of different endmember extraction methods is 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 is more accurate than by some common EE methods.