关键词: 聚类, 红外亮温交叉相关系数, 云迹风

Abstract: Global observation of atmospheric wind fields are potentially the most impotent data in the analysis for numerical prediction. This paper describes the computer image recognition technique of deriving cloud motion winds (CMW) from the IR images of GMS. The first step in the processing is objects classification analysis in an image segment corresponding to an area of 32×32 IR pixels. In this paper, we use direct classification and K-means algorithm to distinguish the high cloud, meddle cloud, low cloud and earth's surface. At the second step we adopt IR cross-correlation technique in the calculation of CMW, the correlation values between the brightness of the template window and the search window are calculated in the search area of the later time image, a cross-correlation coefficient surface is obtained. For the determination of CMW height, the average weight value of IR brightness temperature of object cloud is compared with the climatic statistic vertical temperature profile in this season, and CMW height, i.e. high cloud, meddle cloud or low cloud, is determined.

Key words: classification, IR cross-correlation coefficient, cloud motion wind