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Paper Information

Journal:   IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH   2017 , Volume 23 , Number 4 #a00468; Page(s) 729 To 743.

Evaluation of SVM with Kernel method (linear, polynomial, and radial basis) and neural network for land use classification

* Department of Range and Watershed Management, Faculty of Natural Resources and Desert Studies, Yazd University, Iran
Image classification is always one of the most important issues in remote sensing, and the obtained information from image classification is widely used in this field and other applications like urban planning, natural resource management, agriculture, etc. Since the main purpose of processing satellite images is preparing subjective and practical maps, choosing a suitable classification algorithm has an essential role. This paper studies the efficacy of Support Vector Machines (SVM) algorithm regarding satellite image classifications and compares it to artificial neural network algorithm. SVM is a group of classified and observed mechanical learning algorithms, used in remote sensing. In this study, SVM algorithms were employed for land use classification of Meymeh area using ETM+ landsat data. The classification via SVM was automatically performed by three types of linear Kernel, polynomial, and radial basis. Besides, the performance of this method was compared to that of artificial neural network classification method. Results showed that the average overall accuracy and Kappa coefficient of SVM algorithms, including linear Kernel, polynomial and radial basis, were respectively 9 percent and 12 percent more efficient than artificial neural network classification. Consequently, this study substantiates the efficiency and sufficiency of SVM algorithms in classification of remote sensing images.
Keyword(s): Neural network method,support vector machines,land use,supervised classification,Meymeh
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