Next Article in Journal
Counting Electric Sheep: Understanding Information in the Context of Media Ecology
Previous Article in Journal
The Essence, Classification and Quality of the Different Grades of Information
Information 2012, 3(3), 420-441; doi:10.3390/info3030420

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

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)
View Full-Text   |   Download PDF [2139 KB, 20 September 2012; original version 14 September 2012]   |   Browse Figures


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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

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.

View more citation formats

Article Metrics

For more information on the journal, click here


Cited By

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert