A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels
Uttam Kumar 1,2,3            S. Kumar Raja 4            Chiranjit Mukhopadhyay 3            T.V. Ramachandra 1,2,*
1 Energy and Wetlands Research Group, Centre for Ecological Sciences [CES], 2 Centre for Sustainable Technologies (astra)
3 Department of Management Studies, Indian Institute of Science, Bangalore – 560012, India.
4 Institut de Recherche en Informatique et Systèmes Aléatoires, 35042 Rennes cedex-France & Technicolor Research & Innovation, Cesson Sévigné, France
*Corresponding author:


Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM). HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM) in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP) architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.

Keywords: mixture model; sub-pixel classification; non-linear unmixing; MODIS

Citation : Uttam Kumar, S. Kumar Raja, Chiranjit Mukhopadhyay and T.V. Ramachandra, 2012. A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels., Information 2012, 3(3), 420-441; doi:10.3390/info3030420.
* 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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