Calculation of abundance factors in hyperspectral imaging using genetic algorithm

• Published in: 2008 Canadian Conference on Electrical and Computer Engineering pp. 000837 - 000842
• Main Authors: Farzam, M., Beheshti, S., Raahemifar, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2008
• Subjects: Genetic algorithm; Genetic algorithms; Hyperspectral imaging; Hyperspectral sensors; Image resolution; Layout; Least squares methods; Maximum likelihood estimation; Pixel; Spatial resolution; Spectral analysis; Spectral unmixing; Virtual Dimensionality
• ISBN: 9781424416424; 1424416426
• ISSN: 0840-7789
• DOI: 10.1109/CCECE.2008.4564653

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.