Comparative Analysis of Supervised and Unsupervised Classification on Multispectral Data

Asmala, A. and Shaun, Quegan (2013) Comparative Analysis of Supervised and Unsupervised Classification on Multispectral Data. Applied Mathematical Sciences, 7 (74). pp. 3681-3694. ISSN 1312-885X

Preview

PDF ahmadAMS73-76-2013_published.pdf Download (567kB)

Abstract

The aim of this study is to compare two methods of image classification, i.e. ML (Maximum Likelihood), a supervised method, and ISODATA (Iterative Self- Organizing Data Analysis Technique), an unsupervised method. The former is knowledge-driven, while the latter is data-driven. The former needs a priori knowledge about the study area but the latter does not. In practice, the former can classify land covers with a higher accuracy and therefore is more widely used but there have been very few attempts to investigate this. Here we use both methods in our study area, Selangor, Malaysia and compare the outcomes by means of qualitative and quantitative analyses to have a better understanding of the underlying reasons that drive the performance of both methods.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics Q Science > QC Physics
Divisions:	Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User:	Dr. Asmala Ahmad
Date Deposited:	14 Aug 2013 12:29
Last Modified:	28 May 2015 04:00
URI:	http://eprints.utem.edu.my/id/eprint/9032
Statistic Details:	View Download Statistic

Actions (login required)

View Item