Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels

Abstract

In the literature, there are several methods for multilinear source separation. We find the most popular ones such as nonnegative matrix factorization (NMF), canonical polyadic decomposition (PARAFAC). In this paper, we solved the problem of the hyperspectral imaging with NMF algorithm. We based on the physical property to improve and to relate the output endmembers spectra to the physical properties of the input data. To achieve this,we added a regularization which enforces the closeness of the output endmembers to automatically selected reference spectra. Afterwards we accounted for these reference spectra and their locations in the initialization matrices. To illustrate our methods, we used self-acquired hyperspectral images (HSIs). The first scene is compound of leaves at the macroscopic level. In a controlled environment, we extract the spectra of three pigments. The second scene is acquired from an airplane: We distinguish between vegetation, water, and soil.