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农作物实景监测中的图像数据质量控制方法的研究
(1.中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室;2.广东科学技术职业学院;3.南京邮电大学)
Research on image data quality control method in crop real landscape observation
licuina1, shiguangyu2, yuzhenghong3, baixiaodong4
(1.Meteorological Observation Centre of CMA,;2..State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences;3.Guangdong Institute of Science and Technology;4..Nanjing University of Posts and Telecommunications)
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投稿时间:2018-10-18    修订日期:2019-03-05
中文摘要: 提 要:农作物实景自动监测系统具有自动、非接触、非破坏性等优点,是传统农业气象观测的补充。CCD传感器保护外罩的积水、灰尘以及雾霾、雨雪等天气条件会导致农作物实景自动监测系统探测数据出现错误。因此,开展图像质量控制是合理使用农作物实景自动监测系统资料的基础。本文利用郑州、泰安和固城三地的历史农作物实景图像资料,设计了基于颜色特征参数检测和基于暗通道先验直方图检测的图像数据质量控制方法。通过对2010-2012年三年夏玉米和冬小麦等不同天气条件下得到的农作物实景自动观测资料进行质量控制与应用效果检验。结果表明:两种质控方法均可判断出农作物实景自动监测系统中图像观测资料的异常数据;基于颜色特征参数检测方法可有效识别出像素缺失图像,准确率达100%;基于暗通道先验直方图检测方法能有效识别出污染图像,平均准确率为95.67%,平均召回率为87.5%。该质量控制方法可减小模型估算值与观测数据之间的误差。目前,该方法已应用于省级农业自动观测业务系统。
Abstract:Abstracts:The automatic monitoring system for crops has the advantages of automatic, non- contact and non-destructive, and is a useful supplement to traditional agrometeorological observations. The CCD sensor protects the cover from water, dust, and smog, rain and snow, etc., which may cause observation data of automatic crop monitoring system error. Therefore, the quality control is the basis for the rational use of the automatic crops monitoring system to detect data. Based on the historical crop image detection data of Zhengzhou, Tai''an and Gucheng, this paper designs a quality control method for image data based on color feature parameter detection and dark channel prior histogram detection. According to different weather conditions,we carried out quality control on the summer maize and winter wheat image observation data in 2010-2012, and the effects were tested. The results show that both types of inspection methods can determine the anomaly data of image observation data in the automatic crops monitoring system;The color feature parameter detection method can effectively identify the missing image of pixels, the accuracy rate can reach100%; the average precision is 95.67% and recall average is 87.5%.The proposed method based on the histogram of dark channel can effectively identify the contaminated image; the quality control method can reduce the error between the model estimate and the observed data. At present, this method has been applied to automatic agricultural meteorology observation oprational software.
文章编号:201810180461     中图分类号:    文献标志码:
基金项目:中国气象局沈阳大气环境研究所开放基金课题(2016SYIAE12)和国家自然科学基金项目(61701260)共同资助。第一作者李翠娜,主要从事农业气象自动化观测方法研究工作,Emaillicn1030@126.com
引用文本:
李翠娜,石广玉,余正泓,白晓东,0.[en_title][J].Meteor Mon,():-.
licuina,shiguangyu,yuzhenghong,baixiaodong,0.Research on image data quality control method in crop real landscape observation[J].Meteor Mon,():-.