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Remote Sensing Technology and Application  2019, Vol. 34 Issue (1): 115-124    DOI: 10.11873/j.issn.1004-0323.2019.1.0115
    
Comparative Study of ELM and SVM in Hyperspectral Image Supervision Classification
Mou Duoduo,Liu Lei
(School of Earth Science and Resources,Chang'an University,Xi’an,710064,China)
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Abstract  Combining the spatial features and spectral feature of hyperspectral remote sensing image in supervised classification can effectively improve the classification time and accuracy.In this study,the spatial information extraction method,named watershed transform,was combined with the Extreme Learning Machine(ELM)and Support Vector Machine(SVM)methods.The classification results of the datasets with the spatial features and without the spatial features were synthetically evaluated and compared.Two hyperspectral datasets,the ROSIS data of Pavia university and the Hyperion data of Okavango Delta(Botswana),were selected to test the methods.After preprocessing,the training samples were selected from the images as the reference areas for each type,and the spectral features of each type were analyzed.The two classification methods were utilized to classify the hyperspectral datasets and relevant classification results were obtained.based on the validation samples selected from the images,the classification results were evaluated using the confusion matrix and the execution times.After that,the spectral features and spatial features were combined to classify the data.The results show that the Extreme Learning Machine(ELM) is superior to the Support Vector Machine(SVM)in the classification time and precision,and the spatial features are introduced in the classification process,which can effectively improve the classification accuracy.
Key words:  Hyperspectral remote sensing      Supervised classification      Extreme learning machine      Support vector machine      Classification time and accuracy     
Received:  11 April 2018      Published:  02 April 2019
ZTFLH:  P237  
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Mou Duoduo, Liu Lei. Comparative Study of ELM and SVM in Hyperspectral Image Supervision Classification. Remote Sensing Technology and Application, 2019, 34(1): 115-124.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.1.0115     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I1/115

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