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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| Language: | en |
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Faculty of Social Sciences and Humanities, UKM,Bangi
2009
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| Online Access: | http://journalarticle.ukm.my/917/1/1.2009-1-hasmadi-english.pdf http://journalarticle.ukm.my/917/ http://www.ukm.my/geografia/v1/index.php |
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| author | Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF, |
| author_facet | Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF, |
| author_sort | Mohd Hasmadi I, |
| building | Tun Sri Lanang Library |
| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my-ukm.journal-917 |
| institution | Universiti Kebangsaan Malaysia |
| language | en |
| publishDate | 2009 |
| publisher | Faculty of Social Sciences and Humanities, UKM,Bangi |
| record_format | eprints |
| spelling | my-ukm.journal-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 |
| spellingShingle | Mohd Hasmadi I, Pakhriazad HZ, Shahrin MF, Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data |
| title | 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_short | 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 |
| url | http://journalarticle.ukm.my/917/1/1.2009-1-hasmadi-english.pdf http://journalarticle.ukm.my/917/ http://www.ukm.my/geografia/v1/index.php |
| url_provider | http://journalarticle.ukm.my/ |