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A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

Abstract : The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.
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Jianing Zhao, Wanlin Gao, Zili Liu, Guifen Mou, Lin Lu, et al.. A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm. Third IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture III (CCTA), Oct 2009, Beijing, China. pp.15-20, ⟨10.1007/978-3-642-12220-0_3⟩. ⟨hal-01062132⟩

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