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Using Artificial Neural Network for Classification of High Resolution ‎Remotely Sensed Images and Assessment of Its Performance Compared ‎with Statistical Methods
Current Issue
Volume 2, 2015
Issue 1 (January)
Pages: 1-8   |   Vol. 2, No. 1, January 2015   |   Follow on         
Paper in PDF Downloads: 54   Since Aug. 28, 2015 Views: 731   Since Aug. 28, 2015
Authors
[1]
Saeed Ojaghi, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
[2]
Hamid Ebadi, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
[3]
Farshid Farnood Ahmadi, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
Abstract
Image classification is always one of the most important issues in remote sensing and obtained ‎information from image classification is most widely used in this field and other applications like urban ‎planning, natural resource management, agriculture and etc. The purpose of this study is to assess the ‎performance of multi-layer perceptron neural networks to classify high resolution IKONOS image ‎which covers mainly urban area of Shahriar city which is located in Tehran Province of Iran. The output of a neural network classifier has been compared with the results of support vector machine with the Gaussian kernel function and Maximum Likelihood Classification (MLC) algorithm which is most commonly used in ‎statistical approach image classification. In the best situation, the classification outputs indicate that ‎neural network algorithm, including 0.8775overall accuracy and 0.82 Kappa Coefficient is more ‎accruable and reliable than both the support vector machine with 0.8557 and 0.8197 and maximum likelihood with 0.7836 and 0.7295 overall accuracy and ‎Kappa Coefficient Respectively. Also results indicate that in these three methods, training data and model parameters play ‎important roles in the classification accuracy.‎
Keywords
Artificial Neural Network, Support Vector Machine, Image Classification, Maximum Likelihood, ‎Remote Sensing
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