Estimation of pure spectral signatures, called endmembers, is a key step in hyperspectral image (HI) analysis. We propose endmembers estimation method that uses Gabor Filter Banks (GFBs) to filter HI into set of HIs with different resolutions and orientations. Afterwards, set of approximate pure pixels is identified from each filtered HI by means of directivity based criterion. Hierarchical clustering is used to estimate candidate endmembers from sets of approximate pure pixels. Final estimate of endmembers is obtained after annotation of candidate endmembers with library of pure spectral signatures. Thereby, candidate with the smallest angular deviation with respect to corresponding pure spectra is selected as endmember estimate. Proposed method is compared favorably with five endmembers estimation methods using well-understood experimental AVIRIS Cuprite Nevada dataset.
Hyperspectral imaging, Gabor filters, endmembers, spectral unmixing.