SNPP-VIIRS夜间灯光影像去噪方法研究
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摘要
高精度的夜间灯光数据能够有效反映人类空间活动特征,传统的SNPP-VIIRS夜间灯光影像去噪方法容易忽略新增加或消失的有效灯光,且未处理灯光异常值,随着时间推移,去噪误差会逐渐增大。因此,本文提出了利用中值滤波与低阈值去噪相结合的方法过滤SNPP-VIIRS夜间灯光影像中的异常值及背景噪声,并将去噪后的夜间灯光总量与GDP进行相关性分析及精度验证。试验结果表明,通过中值滤波与低阈值去噪结合的方法提取的夜间灯光总量与GDP的相关性优于传统方法,利用该方法建立的模型估算出的亮度值精度更高,证明该方法具有更高的去噪精度。
The high precision nighttime lights data can effectively reflect the characteristics of human space activities. The traditional SNPP-VIIRS nighttime lights images denoising method is easy to ignore the newly added or disappeared effective light,and has not dealt with the light outliers. With the passage of time,the denoising error will gradually increase. Therefore,this paper proposes to filter the outliers and background noise of SNPP-VIIRS nighttime lights images using a combination of median filtering and low threshold denoising,and the correlation between the total amount of nighttime light after denoising and GDP is analyzed and the accuracy is verified. Experimental results show that the correlation between the total amount of nighttime light collected by median filtering and low threshold denoising and GDP is superior to the traditional method,and the precision of the brightness estimated by the model established by this method is higher,reflecting that the method has a higher denoising precision.
The high precision nighttime lights data can effectively reflect the characteristics of human space activities. The traditional SNPP-VIIRS nighttime lights images denoising method is easy to ignore the newly added or disappeared effective light,and has not dealt with the light outliers. With the passage of time,the denoising error will gradually increase. Therefore,this paper proposes to filter the outliers and background noise of SNPP-VIIRS nighttime lights images using a combination of median filtering and low threshold denoising,and the correlation between the total amount of nighttime light after denoising and GDP is analyzed and the accuracy is verified. Experimental results show that the correlation between the total amount of nighttime light collected by median filtering and low threshold denoising and GDP is superior to the traditional method,and the precision of the brightness estimated by the model established by this method is higher,reflecting that the method has a higher denoising precision.
引文
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[2]沈谦,朱长明,张新,等.植被调节下的干旱区不透水面覆盖率遥感估算方法[J].测绘通报,2018(2):78-82.
[3]胡云锋,赵冠华,张千力.基于夜间灯光与LUC数据的川渝地区人口空间化研究[J].地球信息科学学报,2018,20(1):68-78.
[4]李强,苏奋振,王雯癑.基于VIIRS数据的油气平台提取技术研究[J].地球信息科学学报,2017,19(3):398-406.
[5]郑子豪,陈颖彪,吴志峰,等.单元路网长度的DMSP/OLS夜间灯光数据去饱和方法[J].遥感学报,2018,22(1):161-173.
[6]YU B,SHI K,HU Y,et al.Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(3):1217-1229.
[7]曹子阳.基于夜间灯光影像的GDP空间分布模拟研究及其与PM_(2.5)浓度的相关分析[D].广州:中国科学院广州地球化学研究所,2016.
[8]李德仁,余涵若,李熙.基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析[J].武汉大学学报(信息科学版),2017,42(6):711-720.
[9]潘竟虎,胡艳兴.基于夜间灯光数据的中国多维贫困空间识别[J].经济地理,2016,36(11):124-131.
[10]WU R,YANG D,DONG J,et al.Regional inequality in China based on NPP-VIIRS night-time light imagery[J].Remote Sensing,2018,10(2):240.
[11]DAI Z,HU Y,ZHAO G.The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels[J].Sustainability,2017,9(2):305.DOI:10.3390/su9020305.
[12]兰霞,刘欣鑫,沈焕锋,等.一种消除高密度椒盐噪声的迭代中值滤波算法[J].武汉大学学报(信息科学版),2017,42(12):1731-1737.
[13]柴子为,王帅磊,乔纪纲.基于夜间灯光数据的珠三角地区镇级GDP估算[J].热带地理,2015,35(3):379-385.
[14]李峰,卫爱霞,米晓楠,等.基于NPP-VIIRS夜间灯光数据的河北省GDP空间化方法[J].信阳师范学院学报(自然科学版),2016,29(1):152-156.
[15]ZHU X,MA M,YANG H,et al.Modeling the spatiotemporal dynamics of gross domestic product in China using extended temporal coverage nighttime light data[J].Remote Sensing,2017,9(6):626.
[16]李峰,米晓楠,刘军,等.基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法[J].国土资源遥感,2016,28(3):19-24.
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