A High-dimension Feature Spaces Clustering and Corresponding Weather Classification for Multi-spectral Satellite Images

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.