Neural Network-Based Classification of Scenes from SAR Images Using Spectral Information: An Empirical Study

摘要

The use of a feed-forward artificial neural network (FANN) in classifying scenes from a SAR image, acquired over the KUREX test sites, is presented. Three different types of scenes (river, forest, and grassy fields) are located on the SAR image using an optical image and a ground map. For each type of scene, one hundred segments are located with each segment consisting of 16 x 16 pixels. The texture information of each segment of the image is obtained by computing the spectrum of its intensity distribution, after removing the mean intensity from the individual pixel intensities. A feature vector is then obtained for each segment using 64 samples of the spectrum and the mean value of the intensity distribution of the image segment. Ten different feature vectors from each type or class of scene are used to train a FANN, and the performance of the network is tested using the feature vectors that are not used during the training process. Different types of network architectures are considered in a search for optimal performance, and the results are compared with the classical Bayes classifier. In this investigation, we found that a properly trained FANN provides superior performance in classifying three different types of SAR scenes than does the Bayes classifier.