Analysis of Landsat 5 TM data of Malaysian land covers using ISODATA clustering technique

This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification. ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal distribution, hence requires the class means and c...

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Bibliographic Details
Published in:	2012 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE) pp. 92 - 97
Main Authors:	Ahmad, A., Sufahani, S. F.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-12-2012
Subjects:	Accuracy Classification Correlation Earth Industries ISODATA Landsat Remote sensing Satellites Sea measurements
Online Access:	Get full text
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Summary:	This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification. ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal distribution, hence requires the class means and covariance matrices for each class. In this study, we use ISODATA 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 ISODATA is able to detect eight classes from the study area with 93% agreement with the reference map. The behavior of mean and standard deviation of the classes in the decision space is believed to be one of the main factors that enable ISODATA to classify the land covers with relatively good accuracy.
ISBN:	9781467331142 1467331147
DOI:	10.1109/APACE.2012.6457639