天基星图预处理技术研究  

Space-Based Stellar Image Preprocessing Technology

作　　者：张旭光 刘云猛[1] 谭婵[1] 张锷[1,3] Zhang Xuguang;Liu Yunmeng;Tan Chan;Zhang E(Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China;University of Chinese Academy of Sciences,Beijing 100049,China;Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310024,Zhejiang,China)

机构地区：[1]中国科学院上海技术物理研究所红外探测与成像技术重点实验室,上海200083 [2]中国科学院大学,北京100049 [3]中国科学院大学杭州高等研究院,浙江杭州310024

出　　处：《激光与光电子学进展》2024年第12期412-420,共9页Laser & Optoelectronics Progress

基　　金：上海市基础研究特区计划资助项目(JCYJ-SHFY-2022-004)。

摘　　要：随着人类逐渐将活动范围扩展到宇宙太空,地球外部空间特别是地球同步轨道越来越拥挤,废弃的航天设备、太空活动垃圾等产生大量空间碎片。散落的空间碎片可能引发太空事故,导致航天设备损坏或脱轨,因此空间目标探测系统对保证太空环境安全具有重要意义。星图预处理可以提升图像质量和目标信噪比,这无论是对后续空间目标识别追踪还是航天器导航定姿均有重大意义。主要对图像去噪、背景校正、阈值处理和质心提取这几个方面进行研究,总结现有处理方法和它们的优缺点,并提出相应的改进方法。在图像去噪和背景校正部分,使用真实星图来验证不同算法,利用信噪比增益和背景抑制因子来分析算法处理效果,分析对不同信噪比目标的影响,并提出邻域最大值滤波和改进的背景校正法。在阈值处理部分,分析真实星图所具有的直方图特性,并据此提出基于迭代自适应的阈值方法。在质心提取部分,使用盖亚星表做出基于高斯点扩散函数的模拟星图,在添加白噪声后分析不同算法的亚像素质心提取误差和计算时间。最后,指出未来空间目标识别的迫切需求,并根据本文研究提出相关建议以供参考。With the gradual expansion of human activities into space,Earth’s outer space,especially its geosynchronous orbit,is becoming increasingly crowded.A large amount of space debris is generated from abandoned space equipment and space activity waste.Scattered space debris may cause space accidents,leading to damage or derailment of space equipment.Therefore,space object detection systems are of great significance for ensuring the safety of the space environment.Stellar image preprocessing can improve image quality and target signal-to-noise ratio(SNR),which is significant for space target recognition,space target tracking,spacecraft navigation,and spacecraft attitude determination.This study mainly focuses on image denoising,background correction,threshold processing,and centroid extraction.The existing methods and their advantages and disadvantages are summarized,and the corresponding improvement methods are proposed.For image denoising and background correction,different algorithms are validated using a real stellar image.Additionally,the processing effects are analyzed using SNR gain and background suppression factor,and the effect for the targets with different SNRs are analyzed.Consequently,the neighborhood maximum filtering and improved background correction methods are proposed.In the threshold processing section,we analyze the histogram characteristics of real stellar images and propose an iterative adaptive threshold method based on them.For centroid extraction,we use Gaia data to generate a simulated stellar image based on the Gaussian point spread function.After adding white noise,we analyze the sub-pixel centroid extraction error and calculation time of different algorithms.Finally,based on the study results,the urgent need for future space target recognition is pointed out,and relevant suggestions are proposed.

关 键 词：空间碎片 图像去噪 背景校正 阈值处理 质心提取 

分 类 号：TN96[电子电信—信号与信息处理]