Tropical cyclones pose a serious threat to the economy as well as people's life and property in the southeast coastal areas of China. Stationary satellite imagery is the main data source for tropical cyclone real-time monitoring. On the satellite cloud image

the textures of tropical cyclones are similar to that of other cloud structures

which makes the automatic extraction of tropical cyclones difficult. Base on a vector square

a concept of rotation coefficient is proposed to describe the characteristics of tropical cyclones. Additionlly

a tropical cyclone auto-recognition method is also presented. The Otsu algorithm is used to obtain the segmentation threshold

then rotation coefficient combined with cyclone area and brightness temperature features are implied to recognize tropical cyclone. The contrast experiment of the original vector square method and the improved method was carried on

using the imagery of typhoon HAIKUI. The statistics results generated throughout the life cycle stage shows that

the recognition rate of the improved method are 76%

95% and 78% respectively

which are higher than that of the original method. Experiments show that relative to the vector square algorithm

the segmentations of tropical cyclone are more complete and the recognition rate of tropical cyclones in different development stages is higher.