An approach to fully unsupervised hyperspectral unmixing

In the last few years, unmixing of hyperspectral data has become of major importance. The high spectral resolution results in a loss of spatial resolution. Thus, spectra of edges and small objects are composed of mixtures of their neighboring materials. Due to the fact that supervised unmixing is impossible for extensive data sets, the unsupervised Nonnegative Matrix Factorization (NMF) is used to automatically determine the pure materials, so called endmembers, and their abundances per sample [1]. As the underlying optimization problem is nonlinear, a good initialization improves the outcome [2]. In this paper, several methods are combined to create an algorithm for fully unsupervised spectral unmixing. Major part of this paper is an initialization method, which iteratively calculates the best possible candidates for endmembers among the measured data. A termination condition is applied to prevent violations of the linear mixture model. The actual unmixing is performed by the multiplicative update from [3]. Using the proposed algorithm it is possible to perform unmixing without a priori studies and accomplish a sparse and easily interpretable solution. The algorithm was tested on different hyperspectral data sets of the sensor types AISA Hawk and AISA Eagle.