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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References
Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., Parente, M., Gader, P.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 354–379 (2012)
Yu, J., Chen, D., Lin, Y.: Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery. Int. J. Remote Sens. 38(3), 773–795 (2016)
Li, J., Bioucas-Dias, J.M.: Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 3, p. III-250 (2008)
Li, J., Agathos, A., Zaharie, D., Bioucas Dias, J.M., Plaza, A.: Minimum volume simplex analysis: a fast algorithm for linear hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 53(9), 5067–5082 (2015)
Chang, C., Wu, C., Liu, W., Ouyang, Y.: A new growing method for simplex-based endmember extraction algorithm. IEEE Trans. Geosci. Remote Sens. 44(10), 2804–2819 (2006)
Bioucas-Dias, J.M.: A variable splitting augmented Lagrangian approach to linear spectral unmixing. In: IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS, pp. 1–4 (2009)
Wang, F., Chi, C., Chan, T., Wang, Y.: Nonnegative least-correlated component analysis for separation of dependent sources by volume maximization. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 875–888 (2010)
Zymnis, A., Kim, S., Skaf, J.: Hyperspectral image unmixing via alternating projected sub gradients. In: Record of the Forty-First IEEE Asilomar Conference on In Signals, Systems and Computers, pp. 1164–1168 (2007)
Nascimento, J.M.P., Bioucas-Dias, J.M.: Hyperspectral unmixing algorithm via dependent component analysis. In: IEEE International in Geoscience and Remote Sensing Symposium, pp. 4033–4036 (2007)
Ozkan, S., Kaya, B., Bozdagi Akar, G.: EndNet: sparse autoencoder network for endmember extraction and hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 8(99), 1–5 (2018)
Nascimento, J., Dias, J.: Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(4), 898–910 (2005)
Winter, M.E.: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: SPIE Conference on Imaging Spectrometry, pp. 266–275 (1999)
Ambikapathi, A., Chan, T., Ma, W., Chi, C.: A robust alternating volume maximization algorithm for endmember extraction in hyperspectral images. In: 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (2010)
Wu, h., Chaudhry, F., Liu, W.: Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery. In: Recent Advances in Hyperspectral Signal and Image Processing, pp. 30–62 (2006)
Clark, R.N., Swayze, G.A., Gallagher, A.J., Calvin, W.M.: The U.S. Geological Survey, Digital Spectral Library: Version 1 (0.2 to 3.0um) (1993)
Minka, T.P.: Estimating a Dirichlet distribution. Technical report (2000)
Yang, C., Everitt, J., Bradford, J.: Yield estimation from hyperspectral imagery using Spectral Angle Mapper (SAM). Trans. ASABE 51(2), 729–737 (2008)
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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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