Multi-level adaptive support vector machine classification for tropical tree species

• Main Authors: Chew, W. C., Lau, A. M. S., Kanniah, K. D.
• Format: Article
• Published: Association for Geoinformation Technology 2016
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/id/eprint/70030/; https://www.researchgate.net/publication/305164566_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 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.