天基光学遥感图像的信噪比提升技术综述  

A Review of SNR Enhancement Techniques for Space-Based Remote Sensing Images

作　　者：王智 魏久哲 王芸[1,2] 李强 WANG Zhi;WEI Jiuzhe;WANG Yun;LI Qiang(Beijing Institute of Space Mechanics&Electricity,Beijing 100094,China;Key Laboratory of Advanced Optical Remote Sensing Technology of Beijing,Beijing 100094,China)

机构地区：[1]北京空间机电研究所,北京100094 [2]先进光学遥感技术北京市重点实验室,北京100094

出　　处：《航天返回与遥感》2024年第2期102-113,共12页Spacecraft Recovery & Remote Sensing

基　　金：国家自然科学基金重点项目(62331006)。

摘　　要：随着遥感技术的不断发展,天基光学遥感向全时域、智能化方向发展。微光遥感因需要在夜间和晨昏时段等低照度条件下对地物进行探测,成像具有低对比度、低亮度、低信噪比的特性。针对低信噪比特性会导致大量复杂物理噪声将图像景物特征淹没,严重影响地面目标识别与判读的情况,文章基于遥感成像的全链路物理模型,总结天基光学遥感图像信噪比提升的技术途径,分别对基于传统滤波的方式、基于物理模型的方式、基于深度学习的方式的研究现状进行分析,对比并总结各类方式中主要代表算法之间的特点及差异,对未来天基光学遥感图像信噪比提升的技术发展方向进行展望。With the continuous development of the field of remote sensing,space-based remote sensing is developing in the direction of all-sky and intelligent.Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and morning and night periods,it results in the characteristics of low contrast,low brightness and low signal-to-noise ratio of remote sensing images,among which,low signal-to-noise ratio leads to a large number of complex physical noises drowning the image features,seriously affecting the recognition and interpretation of ground objects.This paper summarizes the actual full-link physical model based on optical remote sensing imaging and the technical approaches to improve the signal-tonoise ratio of remote sensing images,and summarizes the methods based on traditional filtering,physical model and deep learning respectively.By comparing the differences among the main representative algorithms of various methods,the paper summarizes their respective characteristics.The future development direction of the improvement of the signal-to-noise ratio of space-based remote sensing images is forecasted.

关 键 词：去噪算法 全链路模型 信噪比 遥感图像 天基遥感 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]