Generalized Cooccurrence Matrix to Classify IRS-1D Images using Neural Network

This paper presents multispectral texture analysis for classification based on a generalized cooccurrence matrix. Statistical and texture features have been obtained from the first order probability distribution and generalized cooccurrence matrix. The features along with the gray value of the selected pixels are fed into the neural network. Frist, Self Organizing Map (SOM) that is an unsupervised network, has been used for segmentation of IRS-1D images. Then a generalized cooccurrence matrix and first order probability distribution have been extracted from each kind of segments. Texture features have been obtained from generalized cooccurrence matrix. The matrices describe relevant “texture” properties of classes. Next, feature vectors are generated from the extracted features. Then the image is classified by Multilayer Perceptron (MLP) network which has been trained separately using the selected pixels. The method used in this paper has been tested on the IRS-1D satellite image of Iran. The Experimental result is compared to the Maximum Likelihood Classification (MLC) result and it has been shown the MLP method is more accurate than MLC method and also is more sensitive to training sites.