A Novel Approach Based on Fisher Discriminant Null Space for Decomposition of Mixed Pixels in Hyperspectral Imagery

Traditional spectral mixture analysis assumes that each endmember must have a constant spectral signature. However, endmember spectral variability always exists in practical situations, which results in reducing the accuracy of the decomposition of mixed pixels. In order to solve this problem, this letter proposes a new method based on Fisher discriminant null space (FDNS) for decomposition of mixed pixels in hyperspectral imagery. The FDNS searches a linear transformation of the spectra, which makes those endmember spectra to have no variability inside each endmember group but large differences among different endmember groups. Therefore, the negative impact caused by endmember spectral variability on unmixing accuracy can be decreased to a large extent by using the transformed spectra. Experimental results of both simulated and real hyperspectral images demonstrate that the proposed algorithm has a high accuracy for the decomposition of mixed pixels in hyperspectral imagery.