测绘通报 ›› 2019, Vol. 0 ›› Issue (3): 21-26.doi: 10.13474/j.cnki.11-2246.2019.0071

• 学术研究 • 上一篇    下一篇

SNPP-VⅡRS夜间灯光影像去噪方法研究

钟亮, 刘小生, 杨鹏   

  1. 江西理工大学建筑与测绘工程学院, 江西 赣州 341000
  • 收稿日期:2018-05-21 出版日期:2019-03-25 发布日期:2019-04-02
  • 通讯作者: 刘小生。E-mail:lxs9103@163.com E-mail:lxs9103@163.com
  • 作者简介:钟亮(1995-),男,硕士生,主要研究方向为遥感影像处理及应用。E-mail:1655289806@qq.com
  • 基金资助:
    江西省科技厅重点项目(20142BBE50024)

Method for SNPP-VⅡRS nighttime lights images denoising

ZHONG Liang, LIU Xiaosheng, YANG Peng   

  1. School of Architecture Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
  • Received:2018-05-21 Online:2019-03-25 Published:2019-04-02

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。

关键词: SNPP-VⅡRS, 夜间灯光, 中值滤波, 低阈值去噪

Abstract: 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.

Key words: SNPP-VⅡRS, nighttime lights, median filtering, low threshold denoising

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