Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.

This paper presents a method to analyze and calculate the angular glint of targets. By parting the target to very small size cells, using high-frequency approximation, the near-field radar cross section (RCS) is calculated based on the scattering from complex targets and environments (SCTE) system, and the angular glint is calculated by the phase grads. The results show that the angular glint can be calculated exactly in the SCTE system, and this method is correct and efficient. In the near-zone, the far-field theory is not applicable and the angular glint should be calculated by the near-field theory.

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