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

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FATHIZAD H.; SAFARI A.; BAZGIR M.; KHOSRAVI GH.

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH Year:2017 | Volume:23 | Issue:4 Page(s): 729-743

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Title

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

Author(s)

FATHIZAD H. | SAFARI A. | BAZGIR M. | KHOSRAVI GH. | Issue Writer Certificate

Keywords

Neural network methodQ1

support vector machinesQ2

land useQ2

supervised classificationQ2

MeymehQ2

Abstract

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.

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APA: Copy

FATHIZAD, H., SAFARI, A., BAZGIR, M., & KHOSRAVI, GH.. (2017). Evaluation of SVM with Kernel method (linear, polynomial, and radial basis) and neural network for land use classification. IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, 23(4 ), 729-743. SID. https://sid.ir/paper/106810/en

Vancouver: Copy

FATHIZAD H., SAFARI A., BAZGIR M., KHOSRAVI GH.. Evaluation of SVM with Kernel method (linear, polynomial, and radial basis) and neural network for land use classification. IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH[Internet]. 2017;23(4 ):729-743. Available from: https://sid.ir/paper/106810/en

IEEE: Copy

H. FATHIZAD, A. SAFARI, M. BAZGIR, and GH. KHOSRAVI, “Evaluation of SVM with Kernel method (linear, polynomial, and radial basis) and neural network for land use classification,” IRANIAN JOURNAL OF RANGE AND DESERT RESEARCH, vol. 23, no. 4 , pp. 729–743, 2017, [Online]. Available: https://sid.ir/paper/106810/en

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