Research Papers

Subpixel classifiers: fuzzy theory versus statistical learning algorithm

[+] Author Affiliations
Anil Kumar

Indian Institute of Remote Sensing

S.K. Ghosh

Indian Institute of Technology

V.K. Dadhwal

Indian Institute of Remote Sensing

J. Appl. Remote Sens. 1(1), 013517 (June 22, 2007). doi:10.1117/1.2759178
History: Received October 7, 2006; Revised June 1, 2007; Accepted June 20, 2007; June 22, 2007; Online June 22, 2007
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Abstract

A comparative study between two approaches of subpixel classification, based on fuzzy set theory and statistical learning has been carried out. The fuzzy set classifiers investigated in this study are Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) in supervised modes. Further, Support Vector Machines (SVMs) have been used in this study for density estimation as a statistical learning subpixel classifier and Mean Field (MF) method has been used for easy and efficient learning procedure for the SVM. The three algorithms FCM, PCM and SVMs were evaluated in subpixel classification mode and accuracy assessment has been carried out using Fuzzy Error Matrix (FERM). Test on the two sub sets of LISS-III multi-spectral image from Resourcesat -1, (IRS-P6) satellite, indicates that density estimation based on SVM approach is consistent with different data sets and out performs both FCM as well as PCM approach.

© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Anil Kumar ; S.K. Ghosh and V.K. Dadhwal
"Subpixel classifiers: fuzzy theory versus statistical learning algorithm", J. Appl. Remote Sens. 1(1), 013517 (June 22, 2007). ; http://dx.doi.org/10.1117/1.2759178


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