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Information 2012, 3(3), 420-441; doi:10.3390/info3030420
Article

A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels

1,2,3
,
4,5
,
3
 and
1,2,*
1 Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India 2 Centre for Sustainable Technologies, Indian Institute of Science, Bangalore 560012, India 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 5 Technicolor Research & Innovation, 35576 Cesson Sévigné, France
* Author to whom correspondence should be addressed.
Received: 1 July 2012 / Revised: 20 August 2012 / Accepted: 22 August 2012 / Published: 14 September 2012
(This article belongs to the Section Information Theory and Methodology)
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Abstract

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 mixture model; sub-pixel classification; non-linear unmixing; MODIS
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Kumar, U.; Raja, K.S.; Mukhopadhyay, C.; Ramachandra, T. A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels. Information 2012, 3, 420-441.

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