Image Based Robust Target Classification for Passive ISAR

Abstract

This paper presents an automatic and robust, image feature-based target extraction, and classification method for multistatic passive inverse synthetic aperture radar range/cross-range images. The method can be used as a standalone solution or for augmenting classical signal processing approaches. By extracting textural, directional, and edge information as low-level features, a fused saliency map is calculated for the images and used for target detection. The proposed method uses the contour and the size of the detected targets for classification, is lightweight, fast, and easy to extend. The performance of the approach is compared with machine learning methods and extensively evaluated on real target images.