Classification of Textures in Satellite Image Based on NeuralNetwork Methods

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, this paper has presented an algorithm with a 9% of efficiency improvement ascompared with the state where just the spatial features are being used. 26 features of GLCM have been used for the identification of dwelled areas. The problem of identifying various areas was analyzed by a new method and this method yielded desirable results. The results of simulation using MATLAB/IMAGE PROCESSING software on IKONOS database, fromwhich the images have been collected, verify the accuracy of the performance of this system.

Keywords:

textural features , GLCM matrix , MLP Neural Network , Feature Vector , SatelliteImages