Kernel Function in SVM-RFE based Hyperspectral Data band Selection |
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Authors: | Zhang Hankui Huang Bo Yu Le |
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Affiliation: | (1.Department of Geography and Resource Management,The Chinese University of Hongkong,Hongkong,China;
2.Earth System Science Research Center,Tsinghua University,Beijing 100084,China) |
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Abstract: | Supporting vector machine recursive feature elimination (SVM-RFE) has a low efficiency when it is applied to band selection for hyperspectral dada,since it usually uses a non-linear kernel and trains SVM every time after deleting a band.Recent research shows that SVM with non-linear kernel doesn’t always perform better than linear one for SVM classification.Similarly,there is some uncertainty on which kernel is better in SVM-RFE based band selection.This paper compares the classification results in SVM-RFE using two SVMs,then designs two optimization strategies for accelerating the band selection process:the percentage accelerated method and the fixed accelerated method.Through an experiment on AVIRIS hyperspectral data,this paper found:① Classification precision of SVM will slightly decrease with the increasing of redundant bands,which means SVM classification needs feature selection in terms of classification accuracy;② The best band collection selected by SVM-RFE with linear SVM that has higher classification accuracy and less effective bands than that with non-linear SVM;③ Both two optimization strategies improved the efficiency of the feature selection,and percentage eliminating performed better than fixed eliminating method in terms of computational efficiency and classification accuracy. |
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Keywords: | Support Vector Machine Classification Hughes phenomenon Feature selection Recursive Feature Elimination |
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