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CX-6(02)微纳卫星超分辨率成像
引用本文:谭政,相里斌,吕群波,孙建颖,李平付,高爽,尹增山. CX-6(02)微纳卫星超分辨率成像[J]. 遥感学报, 2019, 23(2): 196-204
作者姓名:谭政  相里斌  吕群波  孙建颖  李平付  高爽  尹增山
作者单位:中国科学院计算光学成像技术重点实验室, 北京 100094;中国科学院大学, 北京 100049,中国科学院计算光学成像技术重点实验室, 北京 100094,中国科学院计算光学成像技术重点实验室, 北京 100094;中国科学院大学, 北京 100049,中国科学院计算光学成像技术重点实验室, 北京 100094,中国科学院微小卫星创新研究院, 上海 201203,中国科学院微小卫星创新研究院, 上海 201203,中国科学院微小卫星创新研究院, 上海 201203
基金项目:国家自然科学基金(编号:61505219);中国科学院国防科技创新基金(编号:CXJJ-16S045);青岛市光电智库联合基金资助项目
摘    要:面向微纳卫星高分辨率对地遥感,将超分辨率成像应用于中国整星60公斤级的CX-6(02)微纳卫星设计中,解决因体积和重量限制导致传统长焦距、大口径成像载荷无法应用于微纳卫星的问题。图像获取上,采用高帧频面阵CMOS探测器对同一地物目标多次曝光的方式,利用卫星姿态控制偏差和地速补偿来获得多帧具有亚像元位移的图像;超分辨率重建算法上,在变分贝叶斯框架下提出加权双向差分模型,提高先验概率模型的方向约束性,削弱观测方程求解的病态性。CX-6(02)星成像数据实验结果表明,本文的图像采样方法可获得较为充分的亚像元信息;相比传统的L1范数先验和全变分先验的变分贝叶斯超分辨算法重建结果,本文重建结果对反卷积运算导致的噪声放大具有更好的抑制作用,可获得两倍分辨率提升,有效提高数据质量和应用价值。

关 键 词:CX-6(02)微纳卫星  超分辨率成像  亚像元信息  重建算法  变分贝叶斯  先验模型
收稿时间:2018-01-11

CX-6(02) micro-nano satellite super-resolution imaging
TAN Zheng,XIANG Libin,LYU Qunbo,SUN Jianying,LI Pingfu,GAO Shuang and YIN Zengshan. CX-6(02) micro-nano satellite super-resolution imaging[J]. Journal of Remote Sensing, 2019, 23(2): 196-204
Authors:TAN Zheng  XIANG Libin  LYU Qunbo  SUN Jianying  LI Pingfu  GAO Shuang  YIN Zengshan
Affiliation:Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China;University of Chinese Academy of Sciences, Beijing 100049, China,Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China,Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China;University of Chinese Academy of Sciences, Beijing 100049, China,Key Laboratory of Computation Optical Imaging Technology, Chinese Academy of Sciences, Beijing 100094, China,Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China,Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China and Department Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China
Abstract:Micro-nano satellite is one of the developing trends of remote sensing technology with the advantages of light-weight, small size, low cost. However, because of the limitation of the volume and weight, the traditional high resolution optical imaging payload with long focal length and large aperture is difficult to apply to earth observation of micro-nano satellite. In order to solve this problem, a super-resolution imaging scheme is demonstrated in this paper.
First, in the mode of images acquisition, to obtain multi frame images of the same region, the velocity of imaging relative to the ground should be controlled by satellite attitude. And because the attitude control deviation of satellite is objective, so subpixel displacement information can be generated by pitching, yaw, and rolling random deviations, without to install any other displacement generators; Secondly, In the super-resolution reconstruction algorithm, aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a weighted bi-directional difference prior model to overcome the under-constraint of non-edge regions of image due to total variation prior and L1 norm prior, to further restrain the solving space of the observation equation.
The above scheme is applied to the China''s first super-resolution imaging micro-nano satellite:CX-6(02) micro-nano satellite. The imaging results of this satellite show that: our images acquisition method can obtain sufficient subpixel information, which is approximately uniformly distributed with 0.1 pixel magnitude; The result of super-resolution reconstruction is superior to the same variational bayesian method based on L1 norm prior model and total variation prior model, it is hardly to introduce or amplify the computational noise in the iterative process of the algorithm, effectively weakens the ill-posed property of the deconvolution operation. Make the CX-6(02) satellite''s imaging resolution increased from 2.8 to 1.4 meters in the 700 km orbit altitude, and the whole satellite is only 66 kg.
Except for micro-nano satellite, the design scheme of this paper can also be applied to medium or large optical imaging satellite, it may provide a certain theoretical and experimental support for high resolution earth-observing remote sensing.
Keywords:CX-6(02) micro-nano satellite  super-resolution imaging  images acquisition  reconstruction algorithm  variational Bayes  prior model
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