On the use of image moments for ATR from SAR images

Enhancing target recognition from Synthetic Aperture Radar (SAR) images is a challenging task that cannot be generally solved through a unique and specific sensor configuration or signal processing solution. In particular, solutions exploiting physical target modelling not always are able to deal with complex targets or with small differences between classes. This issue can be solved if image processing techniques are exploited in order to represent the target in a reference domain where small differences and complex structures can have a significant contribution to the target recognition task. The aim of this paper is to provide an overview on the use of image moments for Automatic Target Recognition (ATR) from SAR images. In particular two families of image moments will be considered, pseudo-Zernike and Krawtchouk. Both image moments are computed from orthogonal two-dimensional polynomials that are used as basis to represent the targets’ images. The use of image moments introduces advantages in the sense of computational cost, flexibility, reliability and capabilities to identify different targets. Furthermore, these representations can be made rotational, scale and translational invariant, thus allowing operational robustness of algorithms, for example mitigating the lack of image registration between training and test observations. The capabilities of the image moments are discussed together with experimental validation of algorithms. In particular the performance on the MSTAR dataset of military vehicles will be discussed while the Gotcha 3D dataset will be considered for the civilian vehicles case.