Remote sensing image classification is an important and complex problem.Conventional remote sensing image classification methods are mostly based on Bayes’ subjective probability theory.Because there are many defects

a new tendency is that the mathermatical theory of evidence has been applied to remote sensing image classification.At first

this paper introduces differences between Dempster-Shafer’s evidence theory and Bayes’ subjective probability theory and main definitions and algorithms on D-S evidence theory.Especially degree of belief

degree of plausibility and degree of support are the bridges that D-S evidence theory is used in other fields.It emphatically introduced Support function that D-S evidence theory is used on pattern recognition

and degree of support is applied to classification.We acquire degree of support surfaces according to large classes

such as urban land

farmland

forest land

and water

then use "hard classification" to gain initial result.If initial classification accuracy is fitted to the acquirement

reclassification for degree of support surfaces of less than threshold is conducted until final classification result reaches accuracy.Main advantages of this method are that it can perform reclassification after classification and its classification accuracy is very high.This method has dependable theory

intensive application

easy operation and research potential.