Methods for target detection in SAR images

Abstract

Automatic recognition and classification of man-made objects in SAR (Synthetic Aperture Radar) images have been an active research area because SAR sensors can produce images of scenes in all weather conditions at any time of the day which is not possible with infrared and optical sensors [1, 2]. In this thesis, different feature parameter extraction methods from SAR images are proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix of a ROI (region of interest). Entries of the covariance matrix are used in target detection. In addition, the use of computationally more efficient region codifference matrix for target detection in SAR images is also introduced. Simulation results of target detection in MSTAR (Moving and Stationary Target Recognition) database are presented. The RC and region codifference methods deliver high detection accuracies and low false alarm rates. The performance of these methods is investigated with various distance metrics and Support Vector Machine (SVM) classifiers. It is also observed that the region codifference method produces better results than the commonly used Principle Component Analysis (PCA) method which is used together with SVM. The second part of the thesis offers some techniques to decrease the computational cost of the proposed methods. In this approach, ROIs are filtered by directional filters (DFs) at first as a pre-processing stage. Images are categorized according to the filter outputs. The proposed RC and codifference methods are applied within the categories determined by these filters. Simulation results of target detection in MSTAR database are presented through decisions made with l1 norm distance metric and SVM. The number of comparisons made with the training images using l1 norm distance measure decreases as these images are distributed into categories. Therefore, the computational cost of the previous algorithm is significantly reduced. SAR image classification results based on l1 norm distance metric are better than the results obtained using SVM and they show that the two-stage approach does not reduce the performance rate of the previously proposed method much, especially when codifference features are used.