复杂地形云阴影识别

李彬; 辛晓洲; 张海龙; 胡继超

doi:10.11834/jrs.20176079

技术方法 | 浏览量 : 0 下载量: 571 CSCD: 1

PDF
导出
分享
收藏
专辑

复杂地形云阴影识别
Algorithm for cloud shadow identification on complex terrains
2017年21卷第2期页码：263-272
收稿：2016-03-21，

录用：2016-07-18，

纸质出版：2017-03
DOI： 10.11834/jrs.20176079
稿件说明：

移动端阅览

李彬, 辛晓洲, 张海龙, 等. 复杂地形云阴影识别[J]. 遥感学报, 2017,21(2):263-272. DOI： 10.11834/jrs.20176079.

Bin LI, Xiaozhou XIN, Hailong ZHANG, et al. Algorithm for cloud shadow identification on complex terrains[J]. Journal of Remote sensing, 2017, 21(2): 263-272. DOI： 10.11834/jrs.20176079.

摘要

云阴影检测在遥感科学领域是一个重要研究方向，在许多方面有重要应用。单纯采用光谱阈值法识别云阴影会面临较多困难，难以准确识别，而采用时间序列法，则需耗费较多机时，过程较为繁复。在准确识别云像元的基础上，应用几何学方法识别云阴影是较为准确、实用的方法。目前，应用几何学方法计算起伏地形条件下的云阴影分布和变化特征的研究较少，本文提出了一种基于几何学方法的复杂地形条件下的云阴影识别方法，可以计算云像元对应阴影在复杂地形上的位置。模拟和实验表明，所提出的算法在识别复杂地形条件的云阴影方面具有较好效果，克服了几何法识别云阴影模型的原有缺陷。

Abstract

Many methods for cloud shadow detection in remote sensing have been proposed

such as utilizing the threshold and time series. Adopting the spectrum threshold can encounter many difficulties

such as the selected threshold having significant uncertainty and can be influenced by other factors. The time series method requires much time

and its process is complicated. The geometric method is particularly more accurate and practical. However

few studies have investigated the geometry method to calculate the cloud shadows on complex terrains. Therefore

this paper reports on a method that identifies cloud shadows on complex topography based on the principle of geometrical optics.When a straight line crosses a plane

it intersects with the plane at a point. However

when a straight line crosses a curved surface

it can intersect with the surface at many points. We can abstract the light through the cloud pixels as a straight line

whereas the complex terrain can be treated as a curved surface. When the light arrives on the surface by a cloud

it inevitably intersects with the surface at a certain point. This point is the shadow position casted by the cloud. We combined the previous formula to calculate the cloud shadows on the horizontal surface with DEM

which is based on the above principle of the proposed algorithm.A series of simulations and experiments determined that the proposed algorithm could properly describe the cloud shadows on the complex surface. The actual calculated and visual interpretation results had high consistency in the shadow position

and these had better fitting with the coverage by comprehensive interpretation. The

value was 0.78

whereas the RMSE was 3.49

which were much better than the results that did not consider the terrain effect. Our algorithm had a clearer advantage in terms of comprehensively identifying cloud shadows compared with the results of the threshold method based on images.An algorithm was proposed in this study based on geometry method to calculate the cloud shadows on complex terrain and overcome the existing defects of the geometry method in identifying cloud shadows. This algorithm could obtain the shadow positions of corresponding cloud pixels on a complex terrain

and the distortion of cloud shadows on complex surfaces could be properly described. However

the accuracy of the calculated results could be influenced by many factors

such as the accuracy of cloud detection and thin clouds that cannot be easily detected. However

these clouds can also cast shadows on the ground. Cloud height is an important parameter in the calculation process that largely influences the result

but the acquisition of accurate cloud heights has a certain difficulty level in remote sensing. The DEM and visible band data also have a matching problem.

关键词

Keywords

references

Vittorio A and Emery W J.2002.An automated dynamic threshold cloud-masking algorithm for daytime AVHRR images over land. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1682–1694

Wyser Klaus.2002.Remote sensing of surface solar irradiance with corrections for 3-D cloud effects. Remote Sensing of Environment., 80(2): 272–284

Goodwin N R, Collett L J, Denham R J, Flood N and Tindall D. 2013. Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM+ time series. Remote Sensing of Environment, 134: 50–65

Hutchison K D, Mahoney R L, Vermote E F, Kopp T J, Jackson J M, Sei A and Iisager B D. 2009. A geometry-based approach to identifying cloud shadows in the VIIRS cloud mask algorithm for NPOESS. Journal of Atmospheric and Oceanic Technology, 26(7): 1388–1397

Knudby A, Latifovic R and Pouliot D. 2011. A cloud detection algorithm for AATSR data, optimized for daytime observations in Canada. Remote Sensing of Environment, 115(12): 3153–3164

Liu R G and Yang L. 2013. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sensing of Environment, 133: 21–37

刘希, 许健民, 杜秉玉. 2005. 用双通道动态阈值对GMS-5图像进行自动云检测. 应用气象学报, 16(4): 434–444

Liu X, Xu J M and Du B Y. 2005. A bi-channel dynamic threshold algorithm used in automatically identifying clouds on GMS-5 imagery. Journal of Applied Meteorological Science, 16(4): 434–444

Luo Y, Trishchenko A P and Khlopenkov K V. 2008. Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sensing of Environment, 112(12): 4167–4185

Simpson J J, Jin Z H and Stitt J R. 2000. Cloud shadow detection under arbitrary viewing and illumination conditions. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 972–976

宋小宁, 赵英时. 2003. MODIS图象的云检测及分析. 中国图象图形学报, 8(9): 1079–1083

Song X N and Zhao Y S. 2003. Cloud detection and analysis of MODIS image. Journal of Image and Graphics, 8(9): 1079–1083

王开存, 周秀骥, 刘晶淼. 2004. 复杂地形对计算地表太阳短波辐射的影响. 大气科学, 28(4): 625–633

Wang K C, Zhou X J and Liu J M. 2004. The effects of complex terrain on the computed surface solar short-wave radiation. Chinese Journal of Atmospheric Sciences, 28(4): 625–633

武艳, 银燕, 师春香, 何立, 胡婷婷, 孟俊耀. 2012. 基于动态阈值法的NOAA系列卫星云检测结果检验. 高原气象, 31(3): 745–751

Wu Y, Yin Y, Shi C X, He L, Hu T T and Meng J Y. 2012. Validation of NOAA/AVHRR cloud detections by an automated dynamic threshold cloud-masking algorithm. Plateau Meteorology, 31(3): 745–751

Wyser K, O'Hirok W, Gautier C and Jones C. 2002. Remote sensing of surface solar irradiance with corrections for 3-D cloud effects. Remote Sensing of Environment, 80(2): 272–284

杨昌军, 许健民, 赵凤生. 2008. 时间序列在FY2C云检测中的应用. 大气与环境光学学报, 3(5): 377–391

Yang C J, Xu J M and Zhao F S. 2008. Application of time series in FY2C cloud detection. Journal of Atmospheric and Environmental Optics, 3(5): 377–391

张海龙, 刘高焕, 姚玲, 解修平. 2010. 复杂地形任意天气情形下太阳直接辐射量模拟. 中国沙漠, 30(6): 1469–1476

Zhang H L, Liu G H, Yao L and Xie X P. 2010. Direct solar radiation simulation in all-weather and complex terrain conditions. Journal of Desert Research, 30(6): 1469–1476

Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83–94

Zhu Z and Woodcock C E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152: 217–234

Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159: 269–277

文章被引用时，请邮件提醒。

提交

山地叶面积指数反演理论、方法与研究进展

InSAR生成DEM中WRF大气校正

地质灾区光学立体影像植被与3维地形定量变化检测