Analysis and Classification of Remote Sensing, by Using Wavelet Transform and Neural Network

In this paper, we analysis textures of remote sensing images by taking two reference remote sensing images. We employ the wavelet transform and neural network for analysis and classification respectively. We use (symmlet5) and (cioflet1) mother functions for analyzing the two images, that contains water, forest and earth. The images are gray level and (128 times 128) size. The processing is carried out to divide each image into (16) blocks with size (32 times 32). Each block will be entered to the wavelet mother function, after trying several mother functions, we found that the (Coif1, Sym5) are the best choice. The results are passed to the features extraction (mean, standard deviation, and variance) and the output is then fed as input to the neural network(NN). Finally the result from NN with (Levenberg Marquardt (LM) algorithm) gives the type of texture (forest , earth, and water).