A Novel SAR Image Recognition Algorithm with Rejection Mode via Biomimetic Pattern Recognition

Abstract

Due to increasing demand of the modern battlefield awareness and military reconnaissance, Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has been receiving more and more attention. In this paper, a SAR ATR algorithm with rejection mode based on Local Phase Quantization (LPQ) plus Biomimetic Pattern Recognition (BPR) has been proposed. There are three main steps in the proposed algorithm: firstly, a simple preprocessing procedure based on centroid location is applied to the original SAR image to extract the target’s Region of Interest (ROI). Secondly, Short-term Fourier Transform (STFT) computed over sub-windows at every pixel of the ROI image is performed and the extracted information of the Fourier phase spectrum is quantized as features to overcome the target’s azimuth angle variations. Finally, high dimensional geometry space covering method based on BPR theory is adopted and hyper-sausage neuron is employed to construct geometry coverage for the purpose of recognizing or rejecting the incoming targets. Experiments on the public standard MSTAR database show that this proposed algorithm can achieve relatively high recognition rate while obtaining high rejection rate for unknown incoming targets.