Visual-Cognition-Driven SAR Multiple Targets Robust Feature Extraction, Recognition and Tracking

Abstract

Aiming at the multiple targets recognition and tracking in SAR images, a robust feature extraction method and a combined recognition and tracking method for multi-class slow-moving targets based on visual cognition is presented in this paper. To obtain robust feature and high classification precision, a local multi-resolution analysis and feature extraction method based on the visual attention mechanism and a multiple kernel classifier is studied, which realizes the quick classification with high accuracy for multi-class image targets. According to the recognition result and the corresponding relationship of targets in the adjacent frames, the targets’ motion parameters are estimated utilizing the unscented Kalman filter (UKF) based on the “what” and “where” pathways information processing mechanism. As a result, the high performance tracking of multi-class slow-moving targets in complicated background is realized. The simulation results show that the feature extraction and recognition method has good robustness and high classification correct ratio, the combining recognition and tracking method also has high location precision.