Effect of prior probabilities on maximum likelihood classifier

Abstract

Maximum likelihood classifier is widely used in remote sensing. Many researches have indicated that using prior probabilities call make Maximum likelihood classifier perform better, but it is not true that all individual classes will have higher classification accuracy after prior probabilities are incorporated in MLC. In the present paper, the theoretical analysis about the effect of prior probability on maximum likelihood classifier is given first, including the effect on the discrimination process and classification result. Then a case study is carried out by one approach in which prior probabilities are iteratively modified by using the relative area proportion derived from the last classification as prior probabilities in next classification.