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A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels |
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Uttam Kumar1, 2, S. Kumar Raja5, C. Mukhopadhyay1 and T.V. Ramachandra2, 3, 4, *
Senior Member, IEEE
1 Department of Management Studies, 2 Centre for Sustainable Technologies, 3 Centre for Ecological Sciences,
4 Centre for infrastructure, Sustainable Transport and Urban Planning, Indian Institute of Science, Bangalore – 560012, India.
5 Institut de Recherche en Informatique et Systèmes Aléatoires, 35042 Rennes cedex - France & Technicolor Research & Innovation, Cesson Sévigné, France.
*Corresponding author: cestvr@ces.iisc.ernet.in
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Conclusions
The proposed AL-NLMM algorithm integrates the concept of both linear and non-linear mixing. The method is based on extracting the endmembers from the image, followed by unmixing in the second step and then interpolating the fractions for the whole image based on training data that has both the estimated abundance obtained from second step and real proportion. The results on simulated hyperspectral data shows that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089±0.00215 with LMM and 0.0030±0.0001 with the MLP based NLMM when compared to real class proportions. It may be concluded that influence due to multiple reflections among ground cover targets has to be considered for the abundance estimation. While a linear detection method might work adequately for many scenarios, a non-linear model might perform better. The future work will involve developing methods to obtain pure pixels when there are no endmembers in the scene.
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Citation : Uttam Kumar, Kumar Raja. S., Mukhopadhyay. C. and Ramachandra. T.V., 2011. A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels. Proceeding of the 2011 IEEE Students' Technology Symposium 14-16 January, 2011, IIT Kharagpur., pp. 148-153.
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