基于图像超分辨重建的低轨卫星频谱感知 数据空间分辨率提高方法

doi:10.7523/j.ucas.2020.0033

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (3): 386-392.DOI: 10.7523/j.ucas.2020.0033

• 电子信息与计算机科学 • 上一篇下一篇

基于图像超分辨重建的低轨卫星频谱感知数据空间分辨率提高方法

蔚瑞^1,2,3, 谢卓辰¹, 刘洁¹, 刘会杰¹

1. 中国科学院微小卫星创新研究院, 上海 200120;
2. 中国科学院大学, 北京 100049;
3. 上海科技大学信息科学与技术学院, 上海 200120

收稿日期:2020-04-28 修回日期:2020-05-20 发布日期:2021-06-01
通讯作者: 刘会杰
基金资助:
国家自然科学基金重大研究计划重点项目(91738201)和中国科学院青年创新促进会(2019293)资助

Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution

WEI Rui^1,2,3, XIE Zhuochen¹, LIU Jie¹, LIU Huijie¹

1 Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 200120, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 School of Information Science and Technology, ShanghaiTech University, Shanghai 200120, China

Received:2020-04-28 Revised:2020-05-20 Published:2021-06-01

摘要/Abstract

摘要： 在低轨卫星物联网系统中,卫星感知的频谱数据空间分辨率低,空间电磁环境的细节难以分析。针对该问题,提出将频谱的空间分布情况以二维图像形式处理,根据空间频谱感知数据的特点采用适当的图像超分辨率重建算法,提高了频谱的空间分辨率,增强了频谱态势中的细节。仿真结果表明,根据灰度值可以从图像中直接观察信号的存在性,并且依据频谱数据图像特点选择的双三次插值、基于L₁范数先验的贝叶斯方法和基于匹配图像块的学习方法都有效地提高了频谱数据的空间分辨率,用PSNR评价时,基于L₁范数先验的重建算法效果更好,但是基于匹配图像块的学习方法增强了频谱感知数据中的波纹,从视觉上看,提高细节效果略优。

关键词: 低轨卫星, 频谱感知数据, 图像超分辨率重建, 双三次插值

Abstract: In the low orbit satellite Internet of things system, the spatial resolution of spectrum data perceived by satellites is low, and the details of the spatial electromagnetic environment are difficult to analyze. To solve this problem, this paper proposes to process the spatial distribution of the spectrum in the form of two-dimensional images, and to adopt the appropriate image super-resolution reconstruction algorithm according to the characteristics of spatial spectrum sensing data, so as to improve the spatial resolution of the spectrum, enhance the details in the spectrum situation. Simulation results show that the image signal of existence can be directly observed from the grey value. The bicubic interpolation method chosen according to spectral data characteristics, the Bayesian method based on L₁ norm prior, and the learning method based on image blocks matching can effectively improves the spatial resolution of the spectrum data. When evaluating with PSNR, the reconstruction algorithm based on L₁ norm prior is better. But, the learning method based on image blocks matching enhances the ripples in the spectrum sensing data. From a visual point of view, the effect of improving details is slightly better.

Key words: low orbit satellite, spectrum sensing data, images super-resolution reconstruction, bicubic interpolation

中图分类号:

TN911.7

蔚瑞, 谢卓辰, 刘洁, 刘会杰. 基于图像超分辨重建的低轨卫星频谱感知数据空间分辨率提高方法[J]. 中国科学院大学学报, 2022, 39(3): 386-392.

WEI Rui, XIE Zhuochen, LIU Jie, LIU Huijie. Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(3): 386-392.

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基于图像超分辨重建的低轨卫星频谱感知数据空间分辨率提高方法

Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution

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基于图像超分辨重建的低轨卫星频谱感知 数据空间分辨率提高方法

Spatial resolution improvement of spectrum sensing data of LEO satellite based on image super-resolution

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基于图像超分辨重建的低轨卫星频谱感知数据空间分辨率提高方法