Classification of Satellite Images by Texture-Based Models Modulation Using MLP, SVM Neural Networks and Nero Fuzzy
Gholam Reza Shahriari, Abbas Gharibi, and Azim Rezaei Motlagh
Iranian Oil Terminal Company
Abstract—The purpose of this paper is the automatic identification of various districts in satellite images using the textural feature, while comparing them by two methods of GLCM and Fourier Spectrum. The modulation of discrete violet and GLCM yielded a new method for the identification of the urban areas that is used as a criterion for measuring the development rate in the urban areas using satellite images. Through the modulation of GLCM and spatial features, MLP Neural Network and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes. That is used as a criterion for measuring the development rate in the urban areas using satellite images. The results of simulation using MATLAB/IMAGE PROCESSING software on IKONOS database, from which the images have been collected, verify the accuracy of the performance of this system.
Index Terms—textural features, GLCM matrix, MLP neural network, feature vector, satellite images.
Cite: Gholam Reza Shahriari, Abbas Gharibi, and Azim Rezaei Motlagh, "Classification of Satellite Images by Texture-Based Models Modulation Using MLP, SVM Neural Networks and Nero Fuzzy," International Journal of Electronics and Electrical Engineering, Vol. 1, No. 4, pp. 245-251, December 2013. doi: 10.12720/ijeee.1.4.245-251
Cite: Gholam Reza Shahriari, Abbas Gharibi, and Azim Rezaei Motlagh, "Classification of Satellite Images by Texture-Based Models Modulation Using MLP, SVM Neural Networks and Nero Fuzzy," International Journal of Electronics and Electrical Engineering, Vol. 1, No. 4, pp. 245-251, December 2013. doi: 10.12720/ijeee.1.4.245-251
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