期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B7(/1-4)
页码：1499-1504
出版社：Copernicus Publications
摘要：Maximum Likelihood Method (MLM) has been one of the most traditional classification methods in remote sensing field, but its disadvantages have been also pointed out. While a prior occurrence probability gives a crucial effect to classification results, most of classifications have been conducted on an assumption that each a prior probability of land cover is equal because of insufficient a priori information. And as long as the class showing the highest likelihood is allocated to a pixel, misclassification errors are unavoidable. Authors modified method can estimate a prior probability through EM (Expectation Maximization) algorithm, applying a finite mixture model for a target image histogram. And misclassification errors can be overcome by data fusion model. Validation results demonstrate that data fusion model is effective to improve misclassification errors
关键词：Maximum Likelihood Method (MLM); Mixel; Data Fusion; Decision Tree
