NDLI: Analysis of Maximum Likelihood classification technique on Landsat 5 TM satellite data of tropical land covers

Content Provider	IEEE Xplore Digital Library
Author	Ahmad, A. Quegan, S.
Copyright Year	2012
Description	Author affiliation: Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Melaka, Malaysia (Ahmad, A.) \|\| School of Mathematics and Statistics University of Sheffield Sheffield, United Kingdom (Quegan, S.)
Abstract	The aim of this paper is to carry out analysis of Maximum Likelihood (ML) on Landsat 5 TM (Thematic Mapper) satellite data of tropical land covers. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML.
Starting Page	280
Ending Page	285
File Size	1166728
Page Count	6
File Format	PDF
ISBN	9781467331425
e-ISBN	9781467331432
DOI	10.1109/ICCSCE.2012.6487156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-23
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Bayesian Classification Maximum Likelihood
Content Type	Text
Resource Type	Article

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