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遥感技术与应用  2022, Vol. 37 Issue (2): 408-415    DOI: 10.11873/j.issn.1004-0323.2022.2.0408
大气遥感专栏     
H8/AHI卫星数据的夜间陆地雾自动检测
马慧云1(),李亚楠1,吴晓京2,冉印泽1,鄢俊洁3()
1.中南大学 地球科学与信息物理学院,湖南 长沙 410083
2.国家气象卫星中心,北京 100080
3.北京华云星地通科技有限公司,北京 100081
Automatic Detection of Night Land Fog based on H8/AHI Satellite Data
Huiyun Ma1(),Yanan Li1,Xiaojing Wu2,Yinze Ran1,Junjie Yan3()
1.Department of Surveying and Geo-informatics,Central South University,Changsha 410083,China
2.Nation Satellite Meteorological Center,Beijing 100080,China
3.Beijing Huayun Shinetek Science and Technology Co. ,Ltd. ,Beijing 100081,China
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摘要:

夜间雾已成为交通事故频发的重要隐患,夜间雾检测对防治和减少因雾造成的事故和损失,保障人民生命财产安全具有重要的意义。雾与晴空地表夜间亮温差存在明显差异,二者在亮温差影像上有清晰边缘的特征,算法通过Canny边缘检测获取该边缘混合像元,根据边缘混合像元亮温差均值自动获取二者分离检测阈值,进行夜间陆地雾检测。5天H8/AHI夜间雾算法检测结果平均正确率为93.3%、误警率为29.8%,可靠性因子为67.8%,结果表明算法较适合大面积浓雾检测,但对地表存在的特殊天气如霾、雨雪天、雾发展为低云等情况易虚假报警,如未有相关伴随雾的天气现象出现,检测结果正确率为94.6%、误警率为0.05%、可靠性因子为90.1%。算法优点为可自动确定雾与晴空地表的分离检测阈值,与已有夜间雾自动检测算法相比,该算法检测精度有较大提高。2015年11月27日至12月1日17:00~07:00不同时刻夜间雾时序检测定性验证结果证明,算法适合晨昏时刻遥感影像中已处于夜晚区域的雾检测,可检测出90%左右的雾区;对整幅影像均处于夜间的遥感影像,算法检测结果正确率高达90%以上,定性验证结果进一步证明了算法的稳定性和可靠性。

关键词: 亮温差Canny边缘检测夜间雾检测H8/AHI自动    
Abstract:

Night fog has become an important hidden danger of frequent traffic accidents. Night fog detection is of great significance to prevent and reduce accidents and losses caused by fog, and protect the safety of people's lives and property. There is an obvious boundary between fog and surface of clear sky in the nighttime brightness temperature difference image. Canny edge detection is used to obtain the edge mixed pixel, and the separation detection threshold is automatically obtained by the average brightness temperature difference value of the edge mixed pixel to detect the night land fog. The results of 5-day H8/AHI night fog detection show that the probability of detection is 93.3%, the false alarm ratio is 29.8% and the critical success index is 67.8%. The results show that the algorithm is more suitable for large area dense fog detection, and it is prone to false alarm for special weather such as haze, rainy and snowy days, and fog developing into low cloud. If there is no weather phenomenon associated with fog, the probability of detection is 94.6%, the false alarm ratio is 0.05%, and the critical success index is 90.1%. This algorithm can automatically determine the separation detection threshold of fog and clear sky surface. Compared with the existing automatic detection algorithms of night land fog, this algorithm has higher detection accuracy. The qualitative verification results of night land fog timing detection at different times from 17:00~07:00 on November 27, 2015 to December 1, 2015 show that the algorithm is suitable for the fog detection in the night area of the remote sensing image at dawn and dusk, which can detect about 90% of fog area; for the whole image at night, the algorithm can detect more than 90% of fog area. The results of qualitative verification further prove the stability and reliability of the algorithm.

Key words: Bright temperature difference    Canny edge detection    Nighttime fog detection    H8/AHI    Automatic
收稿日期: 2021-10-26 出版日期: 2022-06-17
ZTFLH:  P343.6  
基金资助: 国家自然科学基金面上基金项目(42071334)
通讯作者: 鄢俊洁     E-mail: lingzi_2002@163.com;yanjj@cma.gov.cn
作者简介: 马慧云(1979-),女,山西稷山人,副教授,主要从事遥感数据图像处理研究。E?mail:lingzi_2002@163.com
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引用本文:

马慧云,李亚楠,吴晓京,冉印泽,鄢俊洁. H8/AHI卫星数据的夜间陆地雾自动检测[J]. 遥感技术与应用, 2022, 37(2): 408-415.

Huiyun Ma,Yanan Li,Xiaojing Wu,Yinze Ran,Junjie Yan. Automatic Detection of Night Land Fog based on H8/AHI Satellite Data. Remote Sensing Technology and Application, 2022, 37(2): 408-415.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0408        http://www.rsta.ac.cn/CN/Y2022/V37/I2/408

图1  研究区域审图号:GS(2016)2923号(注:表示研究区域地面观测站点位置)
图2  夜间不同时刻云顶、雾顶和晴空地表亮温差最大最小值图
图3  边缘混合像元直方图和一阶偏导曲线(a) 边缘混合像元直方图 (b) 一阶偏导曲线
图4  夜间陆地雾自动检测流程
图5  2015年11月30号20:00卫星雾检测结果与地面观测结果叠加图审图号:GS(2016)2923号(注:蓝色为卫星雾检测结果;不同符号形状表示不同的地面观测结果::特浓雾、:浓雾、:雾、:非雾)
日期类别地面数据有雾地面数据非雾PODFARCSI
2015年11月27日卫星检测有雾41710.8720.6340.347
卫星检测非雾6840
2015年11月28日卫星检测有雾94800.8620.4600.497
卫星检测非雾15769
2015年11月29日卫星检测有雾1912810.1280.872
卫星检测非雾0739
2015年11月30日卫星检测有雾281780.9830.2170.772
卫星检测非雾5594
2015年12月1日卫星检测有雾228120.9460.0500.901
卫星检测非雾13705
平均值0.9330.2980.678
检测指标平均值[7]0.7080.0980.652
检测指标平均值[11]0.8450.1850.697
表1  夜间雾检测算法与参考文献检验指标值
图6  时序雾检测结果与H8/AHI假彩色影像叠加图(注:蓝色区域为卫星雾检测结果) 审图号:GS(2016)2923号
1 Eyre J R, Brownscombe J L, Allam R J. Detection of fog at night using Advanced Very High Resolution Radiometer(AVHRR) Imagery[J]. Meteorological Magazine,1984,113(1346):266-271.
2 Ellrod G P. Advances in the detection and analysis of fog at night using GOES multispectral infrared grain[J].Weather and Forecasting,1995,10(3):606-619.
3 Lee J R, Chung C Y, Ou M L. Fog detection using geostationary satellite data: temporally continuous algorithm[J]. Asia-Pacific Journal of Atmospheric Sciences,2011,47(2):113-122.
4 Zhang Shunqian, Yang Xiurong. Remote sensing monitoring technology of thick fog at night based on neural networks and fractal grain[J]. Journal of Applied Meteorological Science, 2005,16(6):804-810.
4 张顺谦,杨秀蓉.基于神经网络和分形纹理的夜间浓雾遥感检测技术[J].应用气象学报, 2005, 16(6):804-810.
5 Zhou Xuan, Zhou Xiaozhong, Wu Yaoping, et al. Detection of nighttime fog using MODIS data[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6): 581-583.
5 周旋, 周晓中, 吴耀平, 等.利用MODIS数据监测夜间雾[J].武汉大学学报:信息科学版, 2008,33(6):581-583.
6 Ma Huiyun, Wang Zhao. The function of DEM in separating fog from cloud based on remote sensing image[J]. Remote Sensing for Land and Resources, 2010, 22(1): 55-59.
6 马慧云, 范冲, 赵向东.基于云雾与晴空地表混合像元的云雾检测算法[J].国土资源遥感, 2010, 22(1): 55-59..
7 Zhang Weikang, Ma Huiyun, Zou Zhengrong, et al. Based on SBDART radiative transfer model radiation fog at night automatically cetect the research and time series analysis[J]. Remote Sensing for Land and Resources, 2014, 26(2): 80-86.
7 张伟康,马慧云,邹峥嵘,等.基于SBDART辐射传输模型的夜间辐射雾自动检测及时间序列分析[J].国土资源遥感, 2014, 26(2): 80-86.
8 Zhang Weikang, Ma Huiyun, Zou Zhengrong, et al.Nighttime radiation fog detection and visibility retrieval based on SBDART and BP neural networks[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2014,15(2):197-202.
8 张伟康,马慧云,邹峥嵘,等.基于SBDART和BP的夜间雾遥感检测和能见度反演[J].解放军理工大学学报(自然科学版), 2014, 15(2):197-202.
9 Wen Xiongfei. The fesearch of dynamical detection method for radiation fog over Land based on remote sensing data[D]. Wuhan: Wuhan University, 2010.[文雄飞,陆地辐射雾遥感动态检测方法研究[D].武汉: 武汉大学,2010.]
10 Chen Wei, Yuan Zhikang, Zhou Hongmei,et al. The segmentation experiment of night fog using GMS-5 IR image[J]. Scientia Meteorologica Sinica, 2004,24(2):193-197.
10 陈伟,袁志康,周红妹,等. GMS-5红外图像上夜间雾的分离实验[J]. 气象科学, 2004, 24(2):193-197.
11 Du Juan, Li Wei, Zhang Penglin. Nighttime terrestrial radiation fog detection using time series remote sensing data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1162-1168.
11 杜鹃,李维,张鹏林. 夜间陆地辐射雾的遥感时序数据检测[J].武汉大学学报(信息科学版),2019, 44(8):1162-1168.
12 Lu Hui. Research on fog identification based on Himawari 8 satellite remote sensing data[D]. Hefei:Anhui University,2019.
12 陆会. 基于葵花8卫星遥感数据的大雾识别研究[D]. 合肥:安徽大学,2019.
13 Xu Yun, Xu Ai Wen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J].Remote Sensing for Land and Resources, 2021, 33(1): 96-101.
13 许赟,许艾文.基于随机森林的遥感影像云雪雾分类检测[J].国土资源遥感,2021,33(1):96-101.
14 Wang Liping, Chen Shaoyong, Dong Anxiang. The distribution and seasonal variations of fog in China[J].Acta Geographica Sinica,2005,60(4):689-697.
14 王丽萍,陈少勇,董安祥.中国雾区的分布及其季节变化[J].地理学报,2005,60(4):689-697.
15 Li Zihua. Studies of fog in China over the past 40 years[J]. Acta Meteorologica Sinica, 2001, 59(5): 616-623.
15 李子华.中国近40年来雾的研究[J].气象学报,2001,59(5):616-624.
16 Hunt G E. Radiative properties of terrestrial clouds at visible and infrared thermal wavelengths[J]. Quraterly Journal of the Royal Meteorological Society, 1973, 99(420): 346-369.
17 Zhang Lingyan. Adaptive edge-detection method research ba-sed on Canny algorithm[D]. Xi'an: Northwest University, 2009.
17 张玲艳.基于canny理论的自适应边缘检测方法研究[D]. 西安:西北大学, 2009.
18 Cermak J, Bendix J. Dynamical nighttime fog/low stratus detection based on meteosat SEVIRI data: A feasibility study[J]. Pure and Applied Geophysics, 2007, 164(7-8):1179-1192.
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