Multi-level adaptive support vector machine classification for tropical tree species
High diversity of tree species in tropical forest is a constraint to achieve satisfactory accuracy in tree species classification, as accuracy reduces with the increasing of target tree species. A new multi-level adaptive classification procedure is introduced in the present study employing Support...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Association for Geoinformation Technology
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/70030/ http://eprints.utm.my/70030/ http://eprints.utm.my/70030/ |
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| Summary: | High diversity of tree species in tropical forest is a constraint to achieve satisfactory accuracy in tree species classification, as accuracy reduces with the increasing of target tree species. A new multi-level adaptive classification procedure is introduced in the present study employing Support Vector Machine (SVM). The experiment handled 20 tropical tree species classification using in-situ hyperspectral data. Three levels of classification were carried out and the final overall classification accuracy was improved to 74.56% from the beginning accuracy produced by SVM itself Result of SVM also has proven its better capability than Maximum Likelihood Classification (MLC) in tropical tree species classification. |
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