Abstract
Hyperspectral imaging (HS) records hundreds of continuous bands for each pixel in an image. Due to coarse spatial resolution of HS, and multiple scattering, the spectral measured by the hyperspectral cameras (HSCs) are mixtures of spectra of materials in each pixel. Thus, at each pixel, a spectral unmixing process is required to utilize an accurate estimation of the number of endmembers, their signatures and their abundances fraction. In this paper, we present a large-scale comparison of endmember extraction algorithms. The algorithms explored Vertex Component Analysis algorithm (VCA), Minimum Volume Simplex Analysis (MVSA), N-FINDR, Alternating Volume Maximization (AVMAX), Pixel Purity Index (PPI), Simplex Identification Via Split Augmented Lagrange (SISAL). Three categories of experiments were carried out; accuracy assessment, robustness to the noise, and execution time. The performance of algorithms was evaluated using two different metrics (MSE and SAD). We use simulated hyperspectral dataset sampled from USGS library - The experimental results show that MVSA and SISAL demonstrate robust performance to the changes in the size of the scene. PPI had the least performance compared with other algorithms. AVMAX and VCA have almost identical performance.
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Elkholy, M.M., Mostafa, M., Ebeid, H.M., Tolba, M.F. (2020). Comparative Analysis of Unmixing Algorithms Using Synthetic Hyperspectral Data. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_93
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DOI: https://doi.org/10.1007/978-3-030-14118-9_93
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