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

Kumar, Uttam and Kumar Raja, S and Mukhopadhyay, C and Ramachandra, TV (2011) A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels. In: Proceeding of the 2011 IEEE Students' Technology Symposium, 14-16 January,2011, lIT Kharagpur.

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Abstract

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.

Item Type:	Conference Paper
Publisher:	IEEE
Additional Information:	Copyright 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Hyperspectral;sub-pixel classification;multi-layer perceptron;non-linear unmixing
Department/Centre:	Division of Biological Sciences > Centre for Ecological Sciences Division of Mechanical Sciences > Centre for Sustainable Technologies (formerly ASTRA)
Date Deposited:	10 Oct 2011 09:17
Last Modified:	10 Oct 2011 09:17
URI:	http://eprints.iisc.ac.in/id/eprint/41257

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