目的 云覆盖着地球上空大部分区域,在地球水循环、地气系统能量平衡和辐射传输过程中有着重要的作用,同时云也是天气气候中最重要、最活跃的因子之一；此外,云覆盖地表信息,导致影像配准、融合等处理过程的很多问题,所以云检测十分重要。方法 基于2015年发射的DSCOVR卫星搭载的EPIC相机数据,针对EPIC数据波段范围较广和影像数据是半球尺度的特点,以云指数法作为基础,提出一种新的面向半球尺度数据的云检测方法。首先,分析EPIC数据各个波段的波段特征,尤其是紫光波段,然后根据云在不同波段的反射特性,以指数的形式完成波段组合进行云检测,再与SVM云检测法和可见光云检测法进行比较,最后利用EPIC L2产品对所获得的云分布图和统计云量值进行结果验证,以正确率、漏检率、误检率和Kappa系数作为参考标准完成精度评定。结果 实际EPIC夏季(2017.07)和冬季(2017.01)数据的实验结果表明,本文方法能够有效地检测到薄云(即使在冬季),且云量和云的分布都最为接近实际。结论 在EPIC影像的云检测过程中,我们提出的云检测法从云分布图和云量结果两个方面都优于可见光云检测法和SVM云检测法,经EPIC L2产品验证,该方法有效、可靠,且本文方法能够快速获得半球范围内云的分布情况,有助于对全球云的动态研究和自然天气预测。
Cloud Detection Method for Hemispherical Scale Data
Zhao Yanhong,Guo Qing,Cheng Shu(Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences;School of Surveying and Mapping Science and Technology,Shandong University of Science and Technology,Qingdao)
Objective The cloud covers most of the Earth"s space and plays an important role in the Earth"s water cycle, the energy balance of the Earth and the radiation transmission. At the same time, the cloud is one of the most vital and active factors in the weather and climate. In addition, the cloud usually covers ground information,which causes many problems and difficulties in the processing of image registration and fusion. So cloud detection is very significant and necessary. Method Based on the EPIC data from the DSCOVR satellite launched in 2015. we research the characteristics of EPIC data, such as the hemisphere scale and the wide range of band spectrum (from ultraviolet bands, visible bands to infrared bands). Then we propose a new cloud detection method for EPIC data with the hemispherical scale in the way of normalized difference cloud index (NDCI). In our method, first, we analyze the different reflection characteristics of different bands, which are determined by the physical properties of objects. Especially, the ultraviolet bands of EPIC data are new. Combined with the applications of EPIC data bands, 340nm, 388nm, 680nm and 780nm are identified as the main research bands. Secondly, we analyze the reflection characteristics of clouds including thin clouds and residual clouds. Based on the above two aspects, we define the cloud index (CI) to detect clouds, which effectively reduces the influence of underlying surface on the cloud detection results. According to the research bands, we design two CI indexes. CI (340) is the difference between the reflectivity of 680nm band and 340nm band divided by the reflectivity of 780nm band. CI (388) is the the difference between the reflectivity of 680nm band and 388nm band divided by the reflectivity of 780nm band. The method is analyzed from the cloud amount and the cloud distribution. Result In order to verify the effectiveness of the proposed cloud detection method, other three cloud detection methods are compared, including the visible light cloud detection method, SVM cloud detection method and traditional NDCI cloud detection method. The EPIC data corresponding to summer (July 3, 2017.) and winter (January 3, 2017.) are used to conduct experiments. .The comparison results consider both the cloud distribution and the cloud amount. In the experimental cloud distribution results, the cloud distribution obtained by the proposed method is most consistent with the cloud distribution in the original EPIC image with the combination of RGB true color. The results of cloud distribution also show that the proposed method effectively detects thin clouds and residual clouds that are not detected by other methods, even in winter and in summer.The traditional NDCI cloud detection method misjudges a large amount of land as the cloud. So, the cloud amount of the traditional NDCI method is not compared. The CI (388) is the optimal band combination for cloud detection in both winter and summer. In July, the cloud amounts of the visible light cloud detection method, the CI method (CI (340), CI (388) and the SVM method are 21.07%, 26.90%, 31.40% and 32.49%, respectively. Except for the visible light method, the maximum difference between the other methods is 5.59%. In January, the cloud amounts of four methods are 30.60%, 35.34%, 38.50% and 31.34%, respectively. To validate the results of the cloud and cloud distribution, the results are verified using the EPIC L2 data including the Reflectivity product, the CF340 product, and the CF388 product. The mean cloud amount of the three products in July is 32.33%. In summer, the cloud distributions of various methods are basically consistent with the cloud distributions of products. The differences between the visible light method, CI method (CI (340), CI (388)) and the SVM method with the product mean are 11.26%, 5.43%, 0.93%, and 0.16%, respectively. The difference of the cloud amount between SVM method and product is the smallest, and the CI (388) is second smallest. The mean cloud amount of products in winter is 37.34%. The difference of the cloud amount between the product and the CI (388) is the smallest, which is 1.16%. Finally, the accuracy evaluation including the correct detection ratio, the missed detection ratio, the false detection ratio and kappa coefficient is completed. Whether it is in winter or summer, the correct detection ratios of four methods are more than 80%,. In winter, the detection accuracy of four methods is lower than that in summer. For the visible light method, the kappa coefficients in summer and winter are 0.84 and 0.79, respectively, which are the lowest of the four methods, and the correct ratios are also the lowest, which are 84.40% and 80.07%, respectively. For the SVM method, The overall accuracy is up to 88.26% and the lowest is 86.01%. The kappa coefficients for summer and winter are 0.88 and 0.86. The cloud distribution of SVM method is closer to that of the EPIC product than that of the visible light method. In the band combination of CI method for summer, the correct ratios of CI (388) and CI (340) are 94.34% and 93.24%, respectively. The correct ratio of CI(388) in winter is as high as 92.96%. CI(388) has the largest kappa coefficient with 0.94 in summer and 0.92 in winter. Therefore, in both winter and summer, CI(388) band combination obtains the best cloud distribution and cloud amount as the EPIC L2 product. Conclusion In the cloud detection process of EPIC image, our proposed cloud detection method is superior to the visible light cloud detection method and the SVM method in both cloud distribution and cloud amount. It is valid and reliable by EPIC L2 product verification. Moreover, the method of this paper can quickly obtain the cloud distribution and cloud amount within the hemisphere, which is helpful for dynamic research and natural weather prediction of global clouds.