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On orbit extraction method of ship target in SAR images based on ultra-lightweight network

  • role： First author第一作者
  • Affiliation：

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

  • Email：leezl0519@163.com
  • Introduction：李宗凌,1985年生, 男, 高级工程师, 研究方向为遥感图像目标检测识别。E-mail: leezl0519@163.com
LI Zongling12,  
  • Affiliation：

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

WANG Luyuan2,  
  • Affiliation：

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

JIANG Shuai2,  
  • Affiliation：

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

WU Yuhang2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China

  • Email：ztzhangqj@163.com
  • Introduction：张庆君，1969年生，男,研究员，研究方向为航天器系统与总体技术、航天遥感。E-mail: ztzhangqj@163.com
ZHANG Qingjun2*

résumé

The low efficiency of orbit data processing and poor effectiveness of remote sensing satellite information acquisition are problems requiring great attention. Meeting the requirements of satellites in orbit in real time and efficient extraction of the targets of interest are challenging endeavors because of their heavy dependence on data transmission and communication bandwidth.In this paper, an efficient ship target detection algorithm based on global statistics and an ultra-lightweight suspected target identification network is designed to achieve the rapid extraction of ship targets. The traditional image processing method is used to detect the target quickly, obtain the suspected target slice, and reduce the amount of data significantly. A self-designed and improved ultra-lightweight identification network based on deep learning is then used to realize a second cycle of suspected target screening and improve the accuracy of target extraction. Much work has been done to simplify the real-time implementation of image-processing methods, such as OTSU threshold calculation, connected domain labeling, and target identification network based on deep learning. The real-time processing accuracy, speed, hardware scale, and heat consumption of the proposed method are well balanced by reasonable optimization of the algorithm flow and calculation method and establishment of an appropriate error analysis model. The calculation cost of the algorithm is low, and the dependence of the real-time implementation of the algorithm on the performance of the hardware platform is reduced greatly, thereby ensuring the excellent overall performance of the algorithm. In particular, network pruning, weight parameter sharing, and quantization are used to reduce the network weight parameter and forward reasoning calculation storage requirements, thus improving the design of the target identification network.GF-3 satellite data were used to test the algorithm, and experimental results showed that the accuracy of ship target extraction could be improved by 20%—98%, the computational complexity could be reduced by 90%, and the real-time performance could be improved by 50%. When the ship target extraction algorithm model was implemented in a low-power embedded circuit, the power consumption of the whole calculation circuit was less than 13 W, and the standby power consumption was less than 1.5 W; these values meet the requirements of full-time operations in orbit.The proposed method takes into account the effectiveness of the algorithm and feasibility of in-orbit real-time processing to improve the energy efficiency ratio and target extraction performance of the system effectively. The proposed algorithm was implemented in the current satellite embedded circuit and the orbit of a new radar test satellite, and the results obtained reflect good application prospects.

mots-clés

ultra-lightweight network;SAR image;target detection;target identify;real time processing on orbit

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