针对传统人工接触式测量方式获取水边线难度大,航测方式临水作业存在风险且空域审批手续复杂,卫星遥感技术时效性差、数据处理自动化程度低等问题,提出基于民用航海雷达技术的新型水边线测绘方法。利用航海雷达、GNSS罗经集成实时水边线测绘系统,构建雷达全自动数据采集与处理理论及技术框架,完成实时水边线测绘系统的研发。典型应用结果表明,基于航海雷达的实时水边线测绘系统绝对定位误差最大值为1.19 m,中误差为0.70 m;重复测量误差最大值为0.97 m,中误差为0.59 m,精度可满足1∶5 000及以下比例尺地形图水边线测绘的要求;利用本系统可实现低成本、实时化、高效率、自动化测绘水边线,有效解决以往作业方式的不足。
Abstract
Traditional method of manual contact measurement is hard to obtain water line; aerial survey method is risky and airspace approval procedures are complex; satellite remote sensing technology is of low time-efficiency and low degree of automation of data processing. In view of this, a water line surveying and mapping system integrating civil marine radar and GNSS compass is proposed. The theory and technical framework of automatic radar data acquisition and processing are constructed to complete the real-time water line surveying and mapping system. Typical application results unveil that the maximum absolute positioning error of the system based on marine radar is 1.19 m, and the mean square error is 0.70 m; the maximum repeated measurement error is 0.97 m, and the mean square error is 0.59 m. The accuracy of the system meets the requirements of water line surveying and mapping of 1∶5 000 and below scale topographic map. The system effectively overcomes the shortcomings of traditional operation modes with its low-cost, real-time, high efficiency and automation.
关键词
实时水边线 /
航海雷达 /
GNSS罗经 /
测绘 /
系统开发
Key words
real-time waterline /
marine radar /
GNSS /
surveying and mapping /
system development
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参考文献
[1] PEKEL J F, COTTAM A, GORELICK N, et al. High-resolution Mapping of Global Surface Water and Its Long-term Changes[J]. Nature, 2016, 540(7633): 418.
[2] WANG Hua, YANG Biao, JIANG Jian-ping, et al. Real-time Extraction of Water Surface Boundary Using Shipborne Radar[J]. International Journal of Remote Sensing, 2020, 41(7): 2739-2758.
[3] VERPOORTER C, KUTSER T, SEEKELL D A, et al. A Global Inventory of Lakes Based on High-resolution Satellite Imagery[J]. Geophysical Research Letters, 2014, 41(18): 6396-6402.
[4] CLEMENT M A,KILSBY C G, MOORE P. Multi-temporal Synthetic Aperture Radar Flood Mapping Using Change Detection[J]. Journal of Flood Risk Management, 2018, 11(2): 152-168.
[5] SUN J, MAO S. River Detection Algorithmin SAR Images Based on Edge Extraction and Ridge Tracing Techniques[J]. International Journal of Remote Sensing, 2011, 32(12): 3485-3494.
[6] 余连生,李智勇,文贡坚,等.遥感图像融合技术在潮间带地形提取中的应用[J].测绘学报,2011,40(5):551-554,562.
[7] 毋 亭,侯西勇.海岸线变化研究综述[J].生态学报,2016,36(4):1170-1182.
[8] 于彩霞, 王家耀, 许 军, 等.海岸线提取技术研究进展 [J].测绘科学技术学报, 2014, 31(3):305-309.
[9] VÖRÖSMARTY C J, GREEN P, SALISBURY J, et al. Global Water Resources: Vulnerability from Climate Change and Population Growth[J]. Science, 2000, 289(5477): 284-288.
[10]刘经南,高柯夫.智能时代测绘与位置服务领域的挑战与机遇[J].武汉大学学报(信息科学版),2017,42(11):1506-1517.
[11]CHEN Wen-qing, WANG Jian-bin, WANG Xue-jun,et al. Detection Probability Analysis of Airborne Radar for Marine Target[C]//Proceedings of the 6th IEEE International Symposium on Microwave, Shanghai, China. October 28-30, 2015.
[12]URMY S S, WARREN J D. Quantitative Ornithology with a Commercial Marine Radar: Standard-Target Calibration, Target Detection and Tracking, and Measurement of Echoes from Individuals and Flocks[J]. Methods in Ecology and Evolution, 2017, 8(7): 860-869.
[13]CHEN Xin-wei, HUANG Wei-min, ZHAO Chen, et al. Rain Detection from X-band Marine Radar Images: A Support Vector Machine-based Approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3): 2115-2123.
[14]HONEGGER D A, HALLER M C. HOLMAN R A. High-resolution Bathymetry Estimates via X-band Marine Radar: 2. Effects of Currents at Tidal Inlets[J]. Coastal Engineering, 2020, 156: 1-17.
基金
国家重点研发计划项目(2017YFC1502604) ;中国长江三峡集团有限公司科研项目(0704168)
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