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Open AccessArticle

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

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Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK
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Robotics Institute, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi 127788, UAE
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Royal Geographical Society (with IBG), 1 Kensington Gore, London SW7 2AR, UK
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Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(8), 775; https://doi.org/10.3390/rs9080775
Received: 1 June 2017 / Revised: 7 July 2017 / Accepted: 26 July 2017 / Published: 29 July 2017
Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms. View Full-Text
Keywords: hyperspectral image; spectral unmixing; endmembers; artificial neural networks; hybrid switch method hyperspectral image; spectral unmixing; endmembers; artificial neural networks; hybrid switch method
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MDPI and ACS Style

Ahmed, A.M.; Duran, O.; Zweiri, Y.; Smith, M. Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching. Remote Sens. 2017, 9, 775.

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