Calculation of abundance factors in hyperspectral imaging using genetic algorithm

Spatial resolution is a limiting factor in satellite imaging systems. It is usually very difficult to successfully interpret objects from a coarse resolution image. Images at such coarse resolutions result in mixed pixels. Mixed-pixel decomposition or spectral unmixing applies to derivation of constituent components, endmembers(EM), and their fractional proportions(abundances) at the subpixel scale. The mathematical intractability of the abundance non-negative constraint results in complex and extensive numerical approaches. Due to such mathematical intractability, many least square error(LSE) based methods are unconstrained and can only produce sub-optimal solutions. In this paper we propose a mixed genetic algorithm and LSE-based EM estimation method (LSEM) to extract the EM matrix and related abundances vectors. We apply the proposed GA-LSEM method to the subject of unmixing hyperspectral data. The experimental results obtained from simulated images show the effectiveness of the proposed method, specifically the robustness to noise.