Semi-blind Hyperspectral Unmixing Using Nonnegative Matrix Factorization

Abstract

In hyperspectral imaging applications, spectral unmixing aims at identifying the constituent materials of a remotely sensed data and estimates its corresponding spectral signature for data exploitation. In this paper, the unmixing is primarily based on a linear mixture version in which every pixel is considered as a sum of definite number of absolutely clear spectra or endmembers, in accordance with means of abundance. Firstly, the number of endmembers in a given scene is determined using hyperspectral signal subspace identification by minimum error (Hysime) algorithm. Then, a vertex component analysis (VCA) method is used for unsupervised endmember extraction. Based on the observation that a negative reflectance is not possible, it is supportive and significant to constrain with nonnegativity. Thus, a nonnegative matrix factorization is applied for decomposing a given scene into its endmembers and abundance matrix. The successfulness of the researched technique is served using the simulated knowledge supported by USGS laboratory collected by the AVIRIS on mineral mining district, Nevada.