Analysis of Maximum Likelihood Classification on Multispectral Data

Asmala, A. (2012) Analysis of Maximum Likelihood Classification on Multispectral Data. Applied Mathematical Sciences, 6 (129-132). pp. 6425-6436. ISSN 1312-885X

[img] PDF
ahmadAMS129-132-2012_published.pdf - Published Version

Download (365kB)


The aim of this paper is to carry out analysis of Maximum Likelihood (ML)classification on multispectral data by means of qualitative and quantitative approaches. 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.

Item Type: Article
Uncontrolled Keywords: ML, Classification, Decision Boundary
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Asmala Ahmad
Date Deposited: 14 Nov 2012 10:13
Last Modified: 21 Jan 2022 11:01
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item