SVM-based Target Recognition from Synthetic Aperture Radar Images using Target Region Outline Descriptors

The work in this paper explores the discriminatory power of target outline description features in conjunction with Support Vector Machine (SVM) based classification committees, when attempting to recognize a variety of targets from Synthetic Aperture Radar (SAR) images. In specific, approximate target outlines are first determined from SAR images via a simple mathematical morphology-based segmentation approach that discriminates target from radar shadow and ground clutter. Next, the obtained outlines are expressed as truncated Elliptical Fourier Series (EFS) expansions, whose coefficients are utilized as discriminatory features and processed by an ensemble of SVM classifiers. In order to experimentally illustrate the merit of the proposed scheme, this work reports classification results on a 3-class target recognition problem using SAR intensity imagery from the well-known Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The novel approach was compared to a selected methods mentioned in the literature in terms of classification accuracy. The results illustrate that only a small amount of EFS coefficients is necessary to achieve recognition rates that rival other established methods and, thus, target outline information can be a powerful discriminatory feature for automatic target recognition applications relevant to SAR imagery.