According to weather sampling data from static GMS-5 images

the projections of IR1

IR2

VS WV in high-dimension feature spaces such as gray degree

grade degree and veins can be clustered.In this way

we can get to know the subject area of each weather sample in the feature spaces

so that we can get the weather classification of each nephogram.In view of the disadvantages of the conventional clustering algorithm

we develop an idea to combine FCM

GA with FSC mutually.In this way

we can not only overcome the local/the global optimum of GA/FCM algorithm

but also confirm the number of clustering centers objectively.Especially to estimate the classifications of the overlapped samples in high-dimensional feature spaces

we can calculate the distance between these samples and the clustering centers to determine their classifications.The type of the pixels in original cloud images can be found out which group in high-dimensional feature spaces the pixels belong to.So that we can make sure its weather area to accomplish the automatic classifications of the weather area.A lot of experimental test to our method has shown good classification effect and the estimated outcome basically conforms to the weather fact.