The 18th International Radar Symposium IRS 2017, June 28-30, 2017, Prague, Czech Republic 978-3-7369-9343-3 ©2017 DGON 1 Efficient Object Classification and Recognition in SAR Imagery Ievgen M. Gorovyi and Dmytro S. Sharapov Department of Microwave Electronics, Institute of Radio Astronomy of NAS of Ukraine 4 Chervonopraporna Str., Kharkov 61002, Ukraine gorovoy@rian.kharkov.ua, sharapov@it-jim.com Abstract: SAR is a very popular instrument for imaging of the ground surface. Possibility of high-resolution image formation makes it superior tool for various information extraction tasks. In the paper, a problem of automatic target recognition is comprehensively analyzed. An idea of azimuth and range target profiles fusion is proposed. It is demonstrated, that usage of a proper image preprocessing with appropriate feature extraction steps allow to achieve a competitive recognition accuracy while keeping a low-dimensionality of feature vectors. Experimental results are discussed for a publicly available MSTAR dataset. Keywords: synthetic aperture radar; automatic target recognition; SAR image, feature extraction; support vector machines; object classification 1. Introduction Synthetic aperture radar (SAR) is a widely used instrument for various remote sensing tasks [1]-[3]. Among them, the problem of automatic target recognition (ATR) has attracted a considerable attention of SAR research community [4]-[5]. It is known, that SAR images significantly differ from optical images [6]. Basically, two main challenges arise. Firstly, existence of the speckle noise due to a coherent acquisition of backscattered signals. Secondly, a varying reflectivity of man-made objects as a function of viewing angle. In this context, a multi-look processing has a great importance [1]-[3]. The problem is that the human visual system (HVS) often fails in real target identification. This explains a high interest in automatic methods for SAR ATR. A typical SAR ATR problem can be divided into three key stages (Fig. 1). Figure 1. Key steps of ATR The first stage is detection. Commonly, some target candidate regions are extracted from the initial SAR images [6]-[7]. Typically, there are a lot of false positives after this step. This can be easily explained due to the fact, that some clutter objects like buildings, trees, bridges also introduce high reflectivity level. This explains the necessity of the second step: discrimination. This stage can be considered as a two-class classification problem [6]-[7]. Finally, the third