Comparison between pixel- and object-based image classification of a tropical landscape using Système Pour l'Observation de la Terre-5 imagery

Based on the Système Pour l'Observation de la Terre-5 imagery, two main techniques of classifying land-use categories in a tropical landscape are compared using two supervised algorithms: maximum likelihood classifier (MLC) and K-nearest neighbor object-based classifier. Nine combinations of sc...

Full description

Saved in:
Bibliographic Details
Published in:Journal of applied remote sensing 2013-08, Vol.7 (1), p.073512-073512
Main Authors: Memarian, Hadi, Balasundram, Siva K, Khosla, Raj
Format: Article
Language:English
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Based on the Système Pour l'Observation de la Terre-5 imagery, two main techniques of classifying land-use categories in a tropical landscape are compared using two supervised algorithms: maximum likelihood classifier (MLC) and K-nearest neighbor object-based classifier. Nine combinations of scale level (SL10, SL30, and SL50) and the nearest neighbor (NN3, NN5, and NN7) are investigated in the object-based classification. Accuracy assessment is performed using two main disagreement components, i.e., quantity disagreement and allocation disagreement. The MLC results in a higher total disagreement in total landscape as compared with object-based image classification. The SL30-NN5 object-based classifier reduces allocation error by 250% as compared with the MLC. Therefore, this classifier shows a higher performance in land-use classification of the Langat basin.
ISSN:1931-3195
1931-3195
DOI:10.1117/1.JRS.7.073512