The SAS Image Data Classification with Minimum Error Probability Bayes Classifier
Wei Lu, Jing Wang, Jing Guan, Heng Fen Yang · Applied Mechanics and Materials · 2012
Due to multipath noise pollution, SAS image consists of two parts : target and noise .They can be described by K + K mixture distribution . How to separate noise data which obey K distribution from the target which also obeys K is a hot topic in SAS image field. This paper used the minimum error probability Bayes classifier to solve this problem, and achieved good results. At the same time, this paper also studied the factors that affect the classification results, such as the absolute value difference of training sample parameters and K distribution parameters.