THE PERFORMANCE OF OBJECT-BASED CLASSIFIERS IN THE CLASSIFICATION OF VHSR IMAGE

doi:10.6046/gtzyyg.2008.02.08

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Abstract

The parallelepiped classifier (PC)，minimum distance classifier (MDC)，Maximum Likelihood Classifier (MLC)，Neural network (NN) and，especially，the newly developed Support Vector Machines (SVM) were assessed in the object-based image analysis of VHSR data. The impacts of kernel configuration on the performance of the SVM and of the selection of training data of the four classifiers were also evaluated. The result reveals that SVM can improve the accuracy significantly，and is by far more stable than other algorithms in the classification of VHSR data based on OBIA.

Keywords Tarim Basin Water resources Remote sensing

TP75

Issue Date: 15 July 2009

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Cite this article:

YU Hai-Yang, GAN Fu-Ping, WU Fa-Dong, DANG Fu-Xing. THE PERFORMANCE OF OBJECT-BASED CLASSIFIERS IN THE CLASSIFICATION OF VHSR IMAGE[J]. REMOTE SENSING FOR LAND & RESOURCES,2008, 20(2): 30-34.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2008.02.08 OR https://www.gtzyyg.com/EN/Y2008/V20/I2/30

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