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 scale l...
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| Main Authors: | , , |
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| 格式: | Article |
| 語言: | English English |
| 出版: |
SPIE
2013
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| 在線閱讀: | http://psasir.upm.edu.my/id/eprint/29469/1/Comparison%20between%20pixel.pdf http://psasir.upm.edu.my/id/eprint/29469/ http://remotesensing.spiedigitallibrary.org/issue.aspx?journalid=96&issueid=926148 |
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| 總結: | 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. |
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