设为首页
加入收藏
用双通道动态阈值对GMS-5图像进行自动云检测
A BI-CHANNEL DYNAMIC THRESHOLD ALGORITHM USED IN AUTOMATICALLY IDENTIFYING CLOUDS ON GMS-5 IMAGERY
-
摘要: 该文用双通道动态阈值法对GMS-5图像进行自动的云检测。在红外通道和可见光通道，分别对每32×32个像元组成的像元阵进行直方图统计，求出区别云和地物值域的阈值，然后对每个像元阵内的逐个像元进行云判识。讨论了在进行直方图分析以求取阈值的过程中，像元阵大小的选取和动态平滑间距的选取对云检测结果的影响。分析结果表明：像元阵大小取32×32时，像元阵所占的地域空间尺度足够小，像元阵内的观测像元样本数足够多，保证了在直方图聚类分析时，每一类含有足够多的样本；GMS-5观测图像的红外通道对原始直方图进行二次平滑时小平滑间距取1.6 K，可见光通道小平滑间距取1.2%，使得确定动态阈值时步长相对小，保证了分析的精度。用目视图像对分析结果进行真实性检验，在中低纬度地区，可见光和红外两个通道都有资料时，该算法的云判识精度较好。在高纬度地区，由于地表温度低，积雪覆盖多，太阳光照角低，该算法的云判识精度较差。Abstract: A bi-channel dynamic threshold algorithm is used to identify clouds on GMS-5 images. Threshold values of clouds and surface objects are gained in visible and infrared window channels by means of a statistic histogram analysis made of 32×32 pixel matrixes, followed by cloud recognition done from pixel to pixel each of the matrixes. The effect on cloud recognition of the chosen segment and bin size for dynamic smoothing in obtaining threshold values is concerned in terms of the histogram analysis. Results show that it is appropriate to choose 32×32 as segment size, thus ensuring a sufficiently large number of samples for each king in histograms clustering analysis; when the histogram is smoothed it is appropriate to choose the small bin at 1.2%(1.6K) for the visible (IR) channel so that a relatively short step length is applied in determining dynamic threshold value for required accuracy. Test of the results by visually examining images shows that with visible and IR data available the recognition precision from the algorithm is quite good at middle and low latitudes but the precision is not so good at higher latitudes where surface is colder, snow cover is large and the sun's elevated angle is low.
-
Key words:
- Cloud recognition;
- Histogram;
- Threshold
-
表 1 像元个数统计
表 2 动态阈值确定情况的分析
表 3 选取不同的平滑间距时阈值为动态阈值、二分法阈值、默认阈值的比例
-
[1] Rossow W B, Garder L C. Validation of ISCCP cloud detections. J Climate, 1993, 6:2370-2393. doi: 10.1175/1520-0442(1993)006<2370:VOICD>2.0.CO;2 [2] Stowe L L, Davis P A, McClain P E. Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for advanced very high resolution radiometer, J Atmos Ocean Technol, 1999, 16:656-681. doi: 10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2 [3] Desbois M, Seze G, Szejwach G. Automatic classification of clouds on meteosat imagery, Application to high-level clouds. J Climate Appl Meteoro, 1982, 21(3):401-412. doi: 10.1175/1520-0450(1982)021<0401:ACOCOM>2.0.CO;2 [4] Saunders R W, Kriebel K T. An improved method of detecting clear sky and cloudy radiances from AVHRR data. Int J Remote Sens, 1988, 9:123-150. doi: 10.1080/01431168808954841 [5] Peak J E, Tag P M. Segmentation of satellite imagery using hierarchical thresholding and neural networks. J Appl Meteor, 1994, 33:605-616. doi: 10.1175/1520-0450(1994)033<0605:SOSIUH>2.0.CO;2 [6] Nowcasting and very short range forecasting SAF, Prototype Scientific Description for Météo-France/CMS. SAF/NWC/MFCMS/MTR/PSD, Issue 1, Rev.1, 2000. [7] Alan V D V, William J E, Senior Member. An automated dynamic threshold cloud-masking algorithm for daytime AVHRR images over land. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(8):1682-1694. doi: 10.1109/TGRS.2002.802455 -
下载:
图(7) / 表(3)
计量
- 摘要浏览量: 3694
- HTML全文浏览量: 710
- PDF下载量: 1871
- 被引次数: 0
