A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels
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:


Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

Keywords— Hyperspectral, sub-pixel classification, multi-layer perceptron, non-linear unmixing

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.
* Corresponding Author :
  Dr. T.V. Ramachandra
Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore – 560 012, INDIA.
Tel : 91-80-23600985 / 22932506 / 22933099,      Fax : 91-80-23601428 / 23600085 / 23600683 [CES-TVR]
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