Cloud fraction is the basis for the application of meteorological satellites.Compared with traditional methods,the existing methods based on Machine Learning(ML) showing better performance in the utilization of all of the characteristics and optical parameters of the satellite cloud.However,at present there has been no cloud classification standard large sample set,which is a critical problem in this area.This forces researchers to build their own small databases,but in the process of building it,samples of some categories are difficult to label,while those of some relative categories are easy to label,thus the distribution of the sample set is complex and uneven,causing the ML to inhibit cloud detection and cloud fraction performance.In order to resolve this problem,in this paper,based on NSMC's HJ-1A/B satellite imagery data,the Transfer Learning (TL) approach is used for cloud detection.More specifically,the researchers labeled a large number of thick cloud samples,built six sets,and used these as auxiliary sample sets;they then labeled a small number of thin cloud samples,built one set,and used this as a task sample set.The results of the study show that TL,when combined with Extreme Learning Machine (ELM),can utilize the knowledge of the sample sets' auxiliary thick cloud to improve the ELM's thin cloud identification accuracy,which was only trained by the thin cloud sample set.This shows that the TL algorithm would be a good choice in a greater number of categories of cloud classification work when the sample set is complex and uneven.