Modification of Cloud Picture Sample and Automatic Identification of Cloud Type Based on Fuzzy Clustering Method

Based on two-dimensional (infared and visible) gray space projection of cloud classification samples, the fuzzy clustering method (FCM) is used to adjust and optimize the characteristic area of cloud classification samples and to reduce the sampling errors. In view of the limitation of conventional FCM in tackling above problems, an improved FCM to use the characteristic mean of cloud samples instead of the random initial clustering center is proposed to avoid the defect to be sensitive to initial clustering center in the conventional FCM and to rectify the distortion of characteristic structure of cloud samples by the clustering results. Therefore, the improved FCM clustering results can reduce the sampling errors and retain the main attributes of cloud classification samples. The classification results can be used to correctly identify land, water, low cloud, middle cloud, cirrus, convective cloud and cumulonimbus, and the segmentation and discrimination results are consistent with the objective facts.