珞珈三号01星在轨处理技术及验证

doi:10.11947/j.AGCS.2024.20230357

测绘学报 ›› 2024, Vol. 53 ›› Issue (4): 599-609.doi: 10.11947/j.AGCS.2024.20230357

珞珈三号01星在轨处理技术及验证

王密¹(), 郭贝贝¹(), 皮英冬¹, 张致齐², 肖晶³, 戴荣凡¹, 项韶¹

^1.武汉大学测绘遥感信息工程国家重点实验室，湖北　武汉　430079
^2.湖北工业大学计算机学院，湖北　武汉　430068
^3.武汉大学计算机学院，湖北　武汉　430072

收稿日期:2023-09-04 修回日期:2024-02-18 发布日期:2024-05-13
通讯作者: 郭贝贝 E-mail:wangmi@whu.edu.cn;gbb_whu@whu.edu.cn
作者简介:王密（1974—），男，博士，教授，博士生导师，主要研究方向为高分辨率光学遥感卫星数据处理与智能服务。E-mail：wangmi@whu.edu.cn
基金资助:
国家重点研发计划(2022YFB3902804);国家自然科学基金(42192583)

On-orbit processing technology and verification of Luojia-3 01 satellite

Mi WANG¹(), Beibei GUO¹(), Yingdong PI¹, Zhiqi ZHANG², Jing XIAO³, Rongfan DAI¹, Shao XIANG¹

^1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
^2.School of Computer Science, Hubei University of Technology, Wuhan 430068, China
^3.School of Computer Science, Wuhan University, Wuhan 430072, China

Received:2023-09-04 Revised:2024-02-18 Published:2024-05-13
Contact: Beibei GUO E-mail:wangmi@whu.edu.cn;gbb_whu@whu.edu.cn
About author:WANG Mi (1974—), male, PhD, professor, PhD supervisor, majors in high-resolution optical satellite imagery data processing and intelligent service. E-mail: wangmi@whu.edu.cn
Supported by:
The National Key Research and Development Program of China(2022YFB3902804);The National Natural Science Foundation of China(42192583)

摘要/Abstract

摘要：

遥感卫星在轨处理技术是推动遥感技术实现实时智能服务的核心环节。针对星上资源受限环境引起的系列处理难题及遥感数据实时处理与信息提取需求，本文首先构建了任务驱动的卫星遥感数据在轨处理框架，以任务需求为核心并协同星地资源建立基于感兴趣区域的星上处理技术体系；然后，面向不同任务层级和应用场景，给出了以位置信息为驱动的基础在轨处理模式、以目标/场景内容和变化事件为驱动的智能处理模式；最后，论述了适配星上资源受限环境的在轨处理关键算法，基于珞珈三号01星星上处理应用程序，验证了典型算法的在轨处理效果。在任务驱动和星地协同机制下，实现了在轨高精度定位、多类型影像产品快速生成、静动态目标信息实时提取及静态和动态影像高倍率近实时压缩。在轨处理显著提高了传统地面处理的效率，能够有效支撑后续卫星遥感实时智能服务。

关键词: 在轨处理, 任务驱动, 星地协同, 实时智能服务, 珞珈三号01星

Abstract:

The on-orbit processing technology of remote sensing satellites stands as a pivotal component driving the achievement of real-time intelligent services in remote sensing technology. In response to the challenges posed by limited on-orbit resources and the growing demands for real-time data processing and information extraction, this paper initially establishes a task-driven framework for on-orbit processing of satellite remote sensing data. The framework places mission requirements at the core and collaborates with space and ground-based resources to establish a comprehensive on-orbit processing system centered around regions of interest (ROIs). In addition, this paper addresses the diverse needs of different task levels and application scenarios by presenting on-orbit processing modes; the basic model driven by location information and the intelligent modes driven by target or scene content and change events. Finally, this paper delves into the critical algorithms necessary for adapting to the resource-constrained on-board environment. Using the on-orbit processing applications (APPs) on the Luojia-3 01 satellite, the performance of these algorithms was verified. Under the framework of task-driven processing and satellite-ground coordination, the Luojia-3 01 satellite successfully achieved high-precision on-orbit geo-positioning, rapid generation of multi-type image products, real-time extraction of information from both static and dynamic targets, and high-rate, near real-time compression of static and dynamic images. The on-orbit processing significantly enhances the efficiency of traditional ground-based processing and effectively supports subsequent real-time intelligent services in satellite remote sensing.

Key words: on-orbit processing, mission-driven, satellite-ground collaboration, real-time intelligent service, Luojia-3 01 satellite

中图分类号:

P237

王密, 郭贝贝, 皮英冬, 张致齐, 肖晶, 戴荣凡, 项韶. 珞珈三号01星在轨处理技术及验证[J]. 测绘学报, 2024, 53(4): 599-609.

Mi WANG, Beibei GUO, Yingdong PI, Zhiqi ZHANG, Jing XIAO, Rongfan DAI, Shao XIANG. On-orbit processing technology and verification of Luojia-3 01 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 599-609.

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参考文献 38

[1]	李劲东. 中国高分辨率对地观测卫星遥感技术进展[J]. 前瞻科技, 2022, 1(1):112-125.
	LI Jindong. Advances in high-resolution earth observation satellite remote sensing technologies in China[J]. Science and Technology Foresight, 2022, 1(1):112-125.
[2]	赵坚, 孟令杰, 王琦, 等. 我国高分辨率对地观测系统建设与发展[J]. 卫星应用, 2022, 131(11):8-13.
	ZHAO Jian, MENG Lingjie, WANG Qi, et al. Construction and development of high resolution earth observation system in China[J]. Satellite Application, 2022, 131(11):8-13.
[3]	赵文波, 李帅, 李博, 等. 新一代体系效能型对地观测体系发展战略研究[J]. 中国工程科学, 2021, 23(6):128-138.
	ZHAO Wenbo, LI Shuai, LI Bo, et al. Development strategy of the new-generation effectiveness-oriented earth-observation system[J]. Strategic Study of CAE, 2021, 23(6):128-138.
[4]	李德仁. 从珞珈系列卫星到东方慧眼星座[J]. 武汉大学学报(信息科学版), 2023, 48(10):1557-1565.
	LI Deren. From the Luojia series satellites to the oriental smart eye constellation[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10):1557-1565.
[5]	杨芳, 刘思远, 赵键, 等. 新型智能遥感卫星技术展望[J]. 航天器工程, 2017, 26(5):74-81.
	YANG Fang, LIU Siyuan, ZHAO Jian, et al. Technology prospective of intelligent remote sensing satellite[J]. Spacecraft Engineering, 2017, 26(5):74-81.
[6]	张兵. 智能遥感卫星系统[J]. 遥感学报, 2011, 15(3):415-431.
	ZHANG Bing. Intelligent remote sensing satellite system[J]. Journal of Remote Sensing, 2011, 15(3):415-431.
[7]	李德仁, 王密, 杨芳. 新一代智能测绘遥感科学试验卫星珞珈三号01星[J]. 测绘学报, 2022, 51(6):789-796.DOI: 10.11947/j.AGCS.2022.20220184.
	LI Deren, WANG Mi, YANG Fang. A new generation of intelligent mapping and remote sensing scientific test satellite Luojia-3 01[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6):789-796. DOI: 10.11947/j.AGCS.2022.20220184.
[8]	乔凯, 智喜洋, 王达伟, 等. 星上智能信息处理技术发展趋势分析与若干思考[J]. 航天返回与遥感, 2021, 42(1):21-27.
	QIAO Kai, ZHI Xiyang, WANG Dawei, et al. Analysis and some thoughts on the development trend of the on-board intelligent information processing technology[J]. Spacecraft Recovery & Remote Sensing, 2021, 42(1):21-27.
[9]	王密, 杨芳. 智能遥感卫星与遥感影像实时服务[J]. 测绘学报, 2019, 48(12):1586-1594.DOI: 10.11947/j.AGCS.2019.20190454.
	WANG Mi, YANG Fang. Intelligent remote sensing satellite and remote sensing image real-time service[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1586-1594. DOI: 10.11947/j.AGCS.2019.20190454.
[10]	李刚, 刘瑜, 张庆君. 多源卫星数据在轨智能融合技术[J]. 中国科学基金, 2021, 35(5):708-712.
	LI Gang, LIU Yu, ZHANG Qingjun. On the development of on-board fusion of multi-source satellite data[J]. Bulletin of National Na-tural Science Foundation of China, 2021, 35(5):708-712.
[11]	ZHOU Guoqing. Future intelligent Earth observing satellites[C]//Proceedings of 2003 Earth Observing Systems. San Diego: SPIE, 2003.
[12]	CRISP N, SMITH K, HOLLINGSWORTH P. Small satellite launch to LEO: a review of current and future launch system[J]. Tran-sactions of Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 2014, 12(29):39-47.
[13]	VANCE L, WOIDA R, STALLINGS J. Responsive imaging surveillance using CubeSat technologies derived from the DARPA SeeMe project[C]//Proceedings of the 28th Annual AIAA/USU Conference on Small Satellites. Logan: AIAA, 2014.
[14]	SCARDERA M, BAKER M, REYNOLDS R, et al. ALTAIR: millennium's DARPA seeme satellite solution technical evolution[C]//Proceedings of the 28th Annual AIAA/USU Conference on Small Satellites. Logan: AIAA, 2014.
[15]	李德仁. 论“互联网＋” 天基信息服务[J]. 遥感学报, 2016, 20(5):708-715.
	LI Deren. The “Internet Plus” space-based information services[J]. Journal of Remote Sensing, 2016, 20(5):708-715.
[16]	李德仁, 沈欣, 李迪龙, 等. 论军民融合的卫星通信、遥感、导航一体天基信息实时服务系统[J]. 武汉大学学报(信息科学版), 2017, 42(11):1501-1505.
	LI Deren, SHEN Xin, LI Dilong, et al. On civil-military integrated space-based real-time information service system[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11):1501-1505.
[17]	李德仁, 王密, 沈欣, 等. 从对地观测卫星到对地观测脑[J]. 武汉大学学报(信息科学版), 2017, 42(2):143-149.
	LI Deren, WANG Mi, SHEN Xin, et al. From earth observation satellite to earth observation brain[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2):143-149.
[18]	MACKEY R, BROWNSTON L, P CASTLE J, et al. Getting diagnostic reasoning off the ground: maturing technology with TacSat-3[J]. IEEE Intelligent Systems, 2010, 25(5):27-35.
[19]	LORENZ E, HALLE W, FISCHER C, et al. Recent results of the firebird mission[C]//Proceedings of the 37th International Symposium on Remote Sensing of Environment. Tshwane: [s.n.], 2017.
[20]	FISCHER C, KLEIN D, KERR G, et al. Data validation and case studies using the TET-1 thermal infrared satellite system[C]//Proceeding of the 36th International Symposium on Remote Sensing of Environment. Berlin: [s.n.], 2015.
[21]	MARTIN V, BLANCHETG, KUBIK P, et al. PLEIADES-HR 1A&1B image quality commissioning: innovative radiometric calibration methods and results[C]//Proceedings of 2013 Earth Observing Systems XVIII. California: SPIE, 2013.
[22]	贺小军, 李竺强, 秦小宝, 等. 吉林一号光谱卫星技术创新与应用成果[J]. 卫星应用, 2020(3):18-26.
	HE Xiaojun, LI Zhuqiang, QIN Xiaobao, et al. Technological innovation and application achievements of Jilin-1 spectral satellite[J]. Satellite Application, 2020(3):18-26.
[23]	汪精华, 王跃, 于龙江, 等. 高分多模卫星星地一体化快速响应系统设计与在轨验证[J]. 航天器工程, 2021, 30(3):58-63.
	WANG Jinghua, WANG Yue, YU Longjiang, et al. Design and on-orbit verification of ground-to-satellite integrated system supporting rapid response of GFDM-1 satellite[J]. Spacecraft Engineering, 2021, 30(3):58-63.
[24]	ZHANG Bing, WU Yuanfeng, ZHAO Boya, et al. Progress and challenges in intelligent remote sensing satellite systems[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15:1814-1822.
[25]	LI Deren, WANG Mi, YANG Fang, et al. Internet intelligent remote sensing scientific experimental satellite LuoJia3-01[J]. Geo-spatial Information Science, 2023, 26(3):257-261.
[26]	李琳, 岳文振, 葛礼晖, 等. 光学遥感微纳卫星在轨实时信息处理系统[J]. 南京航空航天大学学报, 2018, 50(S1):108-111.
	LI Lin, YUE Wenzhen, GE Lihui, et al. In-orbit real-time information processing system for optical remote sensing micro/nano satellites[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2018, 50(S1):108-111.
[27]	王兆魁, 方青云, 韩大鹏. 成像卫星在轨智能处理技术研究进展[J]. 宇航学报, 2022, 43(3):259-270.
	WANG Zhaokui, FANG Qingyun, HAN Dapeng. Research progress of on-orbit artificial intelligence technology for imaging satellite[J]. Journal of Astronautics, 2022, 43(3):259-270.
[28]	王密, 仵倩玉. 面向星群的遥感影像智能服务关键问题[J]. 测绘学报, 2022, 51(6):1008-1016.DOI: 10.11947/j.AGCS.2022.20220186.
	WANG Mi, WU Qianyu. Key problems of remote sensing images intelligent service for constellation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6):1008-1016. DOI: 10.11947/j.AGCS.2022.20220186.
[29]	POLI D, TOUTIN T. Review of developments in geometric modelling for high resolution satellite pushbroom sensors[J]. The Photogrammetric Record, 2012, 27(137):58-73.
[30]	张祖勋, 姜慧伟, 庞世燕, 等. 多时相遥感影像的变化检测研究现状与展望[J]. 测绘学报, 2022, 51(7):1091-1107. DOI: 10.11947/j.AGCS.2022.20220070.
	ZHANG Zuxun, JIANG Huiwei, PANG Shiyan, et al. Review and prospect in change detection of multi-temporal remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7):1091-1107. DOI: 10.11947/j.AGCS.2022.20220070.
[31]	WANG Mi, CHENG Yufeng, CHANG Xueli, et al. On-orbit geometric calibration and geometric quality assessment for the high-resolution geostationary opticalsatellite GaoFen4[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125:63-77.
[32]	王密, 田原, 程宇峰. 高分辨率光学遥感卫星在轨几何定标现状与展望[J]. 武汉大学学报(信息科学版), 2017, 42(11):1580-1588.
	WANG Mi, TIAN Yuan, CHENG Yufeng. Development of on-orbit geometric calibration for high resolution optical remote sensing satellite[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11):1580-1588.
[33]	张致齐. 任务驱动的高分辨率光学遥感影像星上实时处理关键技术研究[D]. 武汉: 武汉大学, 2018.
	ZHANG Zhiqi. Research on key technologies of task-driven on-board real-time processing of high resolution optical satellite imagery[D]. Wuhan: Wuhan University, 2018.
[34]	曹金山, 张致齐, 王密. 一种带有地理编码的光学卫星视频稳像方法及系统: CN111010494B[P]. 2020-11-03.
	CAO Jinshan, ZHANG Zhiqi, WANG Mi. Optical satellite video image stabilization method and system with geocodes: CN111010494B[P]. 2020-11-03.
[35]	DONG Zhipeng, WANG Mi, WANG Yanli, et al. Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):2104-2114.
[36]	BLANES I, MAGLI E, SERRA-SAGRISTA J. A tutorial on image compression for optical space imaging systems[J]. IEEE Geoscience and Remote Sensing Magazine, 2014, 2(3):8-26.
[37]	XIAO Jing, ZHU Rong, HU Ruimin, et al. Towards real-time service from remote sensing: compression of earth observatory video data via long-term background referencing[J]. Remote Sensing, 2018, 10(6):876.
[38]	LIAO Liang, XIAO Jing, LI Yating, et al. Learned representation of satellite image series for data compression[J]. Remote Sensing, 2020, 12(3):497.

地物类型	序号	广义噪声/(%)
地物类型	序号	原始影像	辐射校正后影像
海洋	1	9.56	2.05
	2	10.38	1.09
	3	10.04	1.15
陆地	1	10.80	1.72
	2	11.17	1.62
	3	9.93	2.57

影像	成像时间	中心经纬度	内部几何精度/像素
			沿轨方向	垂轨方向	平面
			Test1	2023-04-30	109.94°E，41.99°N	0.864	0.603	1.054
Test2	2023-04-30	109.60°E，40.79°N	0.777	0.950	1.227
Test3	2023-05-01	44.67°E，34.39°N	1.024	0.786	1.291
Test4	2023-05-01	44.04°E，32.13°N	0.872	0.990	1.319
Test5	2023-05-03	33.84°E，45.04°N	0.675	1.041	1.241
Test6	2023-05-03	33.67°E，44.57°N	0.777	1.001	1.267

方法	影像大小/像素	相对辐射校正耗时/s	传感器校正耗时/s	系统几何校正耗时/s	总耗时/s	加速比
传统标准景方法	4000×4000	11.045	166.837	379.988	557.870	1.0
面向ROI方法	4000×4000	3.353	0.838	0.108	4.300	129.7

目标	发现率/(%)	虚警率/(%)
飞机	96.61	4.35
油桶	95.24	5.05
船舶	94.54	5.56
平均	95.46	4.98

影像	图像大小/像素	检测耗时/s
1	2000×2000	0.051
2	2000×2000	0.049
3	2000×2000	0.050
4	2000×2000	0.052
平均值		0.051

珞珈三号01星在轨处理技术及验证

On-orbit processing technology and verification of Luojia-3 01 satellite

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参考文献 38

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视频名称	图像大小/像素	跟踪精度/(%)	跟踪耗时/s
港口1	980×1238	91	0.025
港口2	3328×1832	85	0.040
机场1	5304×3008	86	0.067
机场2	1536×1080	95	0.033
平均值		89	0.041

图像名称	图像大小/像素	压缩耗时/s	压缩倍数	PSNR/dB	SSIM
迪拜	3840×2160	0.388	30.20	36.304	0.927
港口	3840×2160	0.463	42.00	36.200	0.923
机场	3840×2160	0.351	30.30	36.559	0.927
拉萨	3840×2160	0.392	31.90	36.450	0.942
马斯喀特	3840×2160	0.377	32.33	36.397	0.901

图像名称	图像大小/像素	压缩耗时/s	压缩倍数	PSNR/dB	SSIM
迪拜	1920×1080	0.067	217.3	37.4	0.93
港口	2664×1520	0.067	211.3	39.5	0.95
机场	1920×1080	0.063	207.4	36.2	0.89
拉萨	1976×1136	0.063	220.0	37.2	0.94
马斯喀特	2360×1344	0.071	212.6	37.2	0.91

[1]	谢卫莹, 王子璇, 李云松. 高效通信的在轨分布式高光谱图像处理[J]. 测绘学报, 2024, 53(4): 589-598.
[2]	李德仁, 王密, 杨芳. 新一代智能测绘遥感科学试验卫星珞珈三号01星[J]. 测绘学报, 2022, 51(6): 789-796.
[3]	王密, 仵倩玉. 面向星群的遥感影像智能服务关键问题[J]. 测绘学报, 2022, 51(6): 1008-1016.
[4]	吴立新, 李佳, 苗则朗, 王威, 陈必焰, 李志伟, 戴吾蛟, 许文斌. 冰川流域孕灾环境及灾害的天空地协同智能监测模式与方向[J]. 测绘学报, 2021, 50(8): 1109-1121.
[5]	李德仁. 展望5G/6G时代的地球空间信息技术[J]. 测绘学报, 2019, 48(12): 1475-1481.
[6]	王密, 杨芳. 智能遥感卫星与遥感影像实时服务[J]. 测绘学报, 2019, 48(12): 1586-1594.
[7]	朱杰, 游雄, 夏青. 任务驱动的面向对象战场环境仿真过程建模[J]. 测绘学报, 2018, 47(10): 1385-1395.