SAR Image Classification by Multilayer Back Propagation Neural Network

A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.