SAR Fast Target Imaging in Sparse Field Based on AlexNet

Satellite borne Synthetic Aperture Radar (SAR) has become an indispensable means of earth observation since its ability of all-weather and all-weather observation. However, SAR imaging still requires a lot energy and computing resource at present. Nowadays the satellite SAR has been developed greatly in both distributed and intelligent imaging. The traditional satellite SAR imaging method is to process the echo of the received LFM signal in the two-dimensional along the azimuth and distance directions. Due to the targets in the ocean are usually sparsed, it is an effective method to detect the region of interest and targets by analyzing the raw echo signal. In this paper, we propose a method based on convolutional neural network to detect the ROI targets from the raw echo data, so as to achieve rapid detection and imaging of targets. In addition, the new method could greatly improve the target recognition accuracy and imaging efficiency compared with traditional SAR imaging methods.