Minimum volume simplicial enclosure for spectral unmixing of remotely sensed hyperspectral data

Abstract

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) end member abundance estimation in mixed pixels. One of the main problems concerning the identification of spectral endmembers is the lack of pure spectral signatures in real hyperspectral data due to spatial resolution and mixture phenomena happening at different scales. In this paper, we present a new method for endmember estimation which does not assume the presence of pure pixels in the input data. The method minimizes the volume of an enclosing simplex in the reduced space while estimating the fractional abundance of vertices in simultaneous fashion, as opposed to other volume-based approaches such as N-FINDR which inflate the simplex of maximumvolume that can be formed using available image pixels. Our experimental results and comparisons to other endmember extraction algorithms indicate promising performance of the method in the task of extracting endmembers from real hyperspectral data. In our experiments, we use laboratory-simulated forest scenes with known endmembers and fractional abundances due to their acquisition in a controlled environment using a real hyperspectral imaging instrumen