Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data

Several methods exist for remote sensing image classification. They include supervised and unsupervised approaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data. Without accuracy assessment the quality of map or output produced wo...

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Main Authors:	Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF
Format:	Article
Language:	English
Published:	Faculty of Social Sciences and Humanities, UKM,Bangi 2009
Online Access:	http://journalarticle.ukm.my/917/ http://journalarticle.ukm.my/917/ http://journalarticle.ukm.my/917/1/1.2009-1-hasmadi-english.pdf

id	ukm-917
recordtype	eprints
spelling	ukm-9172016-12-14T06:28:18Z http://journalarticle.ukm.my/917/ Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF, Several methods exist for remote sensing image classification. They include supervised and unsupervised approaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data. Without accuracy assessment the quality of map or output produced would be of lesser value to the end user. However, supervised and unsupervised techniques show different levels of accuracy after accuracy assessment was conducted. This paper describes a study that was carried out to perform supervised and unsupervised techniques on remote sensing data for land cover classification and to evaluate the accuracy result of both classification techniques. The study used SPOT 5 satellite image taken on January 2007 for 270 / 343 (path / row) as a primary data and topographical map and land cover maps as supporting data. The land cover classes for the study area were classified into 5 themes namely vegetation, urban area, water body, grassland and barren land. Ground verification was carried out to verify and assess the accuracy of classification. A total of 72 sample points were collected using Systematic Random Sampling. The sample point represented 25% of the total study area. The results showed that the overall accuracy for the supervised classification was 90.28% where Kappa statistics was 0.86, while the unsupervised classification result was 80.56% accurate with 0.73 Kappa statistics. In conclusion, this study found that the supervised classification technique appears more accurate than the unsupervised classification. Faculty of Social Sciences and Humanities, UKM,Bangi 2009-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/917/1/1.2009-1-hasmadi-english.pdf Mohd Hasmadi I, and Pakhriazad HZ, and Shahrin MF, (2009) Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geografia : Malaysian Journal of Society and Space, 5 (1). pp. 1-10. ISSN 2180-2491 http://www.ukm.my/geografia/v1/index.php
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Several methods exist for remote sensing image classification. They include supervised and unsupervised approaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data. Without accuracy assessment the quality of map or output produced would be of lesser value to the end user. However, supervised and unsupervised techniques show different levels of accuracy after accuracy assessment was conducted. This paper describes a study that was carried out to perform supervised and unsupervised techniques on remote sensing data for land cover classification and to evaluate the accuracy result of both classification techniques. The study used SPOT 5 satellite image taken on January 2007 for 270 / 343 (path / row) as a primary data and topographical map and land cover maps as supporting data. The land cover classes for the study area were classified into 5 themes namely vegetation, urban area, water body, grassland and barren land. Ground verification was carried out to verify and assess the accuracy of classification. A total of 72 sample points were collected using Systematic Random Sampling. The sample point represented 25% of the total study area. The results showed that the overall accuracy for the supervised classification was 90.28% where Kappa statistics was 0.86, while the unsupervised classification result was 80.56% accurate with 0.73 Kappa statistics. In conclusion, this study found that the supervised classification technique appears more accurate than the unsupervised classification.
format	Article
author	Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF,
spellingShingle	Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF, Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
author_facet	Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF,
author_sort	Mohd Hasmadi I,
title	Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
title_short	Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
title_full	Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
title_fullStr	Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
title_full_unstemmed	Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
title_sort	evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data
publisher	Faculty of Social Sciences and Humanities, UKM,Bangi
publishDate	2009
url	http://journalarticle.ukm.my/917/ http://journalarticle.ukm.my/917/ http://journalarticle.ukm.my/917/1/1.2009-1-hasmadi-english.pdf
first_indexed	2023-09-18T19:31:52Z
last_indexed	2023-09-18T19:31:52Z
_version_	1777405014447226880

Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data

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