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Reseach Article

A Novel Classification Technique for Accuracy Assessment Applied to Digital Imagery

Published on September 2015 by Bindu K., Jayanth J., Ashok Kumar, Shivaprakash Koliwad
National Conference “Electronics, Signals, Communication and Optimization"
Foundation of Computer Science USA
NCESCO2015 - Number 4
September 2015
Authors: Bindu K., Jayanth J., Ashok Kumar, Shivaprakash Koliwad
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Bindu K., Jayanth J., Ashok Kumar, Shivaprakash Koliwad . A Novel Classification Technique for Accuracy Assessment Applied to Digital Imagery. National Conference “Electronics, Signals, Communication and Optimization". NCESCO2015, 4 (September 2015), 14-19.

@article{
author = { Bindu K., Jayanth J., Ashok Kumar, Shivaprakash Koliwad },
title = { A Novel Classification Technique for Accuracy Assessment Applied to Digital Imagery },
journal = { National Conference “Electronics, Signals, Communication and Optimization" },
issue_date = { September 2015 },
volume = { NCESCO2015 },
number = { 4 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 14-19 },
numpages = 6,
url = { /proceedings/ncesco2015/number4/22316-5339/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference “Electronics, Signals, Communication and Optimization"
%A Bindu K.
%A Jayanth J.
%A Ashok Kumar
%A Shivaprakash Koliwad
%T A Novel Classification Technique for Accuracy Assessment Applied to Digital Imagery
%J National Conference “Electronics, Signals, Communication and Optimization"
%@ 0975-8887
%V NCESCO2015
%N 4
%P 14-19
%D 2015
%I International Journal of Computer Applications
Abstract

This study is to classify satellite data based on supervised fuzzy classification technique. Attempts to classify remote sensed data with traditional statistical classification technique faced number of challenges as the traditional per-pixel classifier examine only the spectral variance ignoring the spatial distribution of the pixels, corresponding to the land cover classes and correlation between bands causes problems in classifying the data and its result. Hence in this work, we use fuzzy classification. this makes no assumption about stastical distribution of the data & it provides more complete information for a thorough image analysis. The results show that fuzzy supervised technique algorithm showed an improvement of more than 5% of accuracy at 12 classes on comparison with MLC.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Supervised Classification Mlc Remote Sensing