Remote sensing image classification based on formal concept analysis
基于形式概念分析的遥感影像分类

In order to solve the problems that how to mine and express classification knowledge and rules in current remote sensing image classification, this paper introduces a new data mining theory of formal concept analysis, and realizes the connotation reduction of concept based on the minimum coverage of sets for ensuring the simplicity of classification rules. Meanwhile, the Fang city of Hubei province is selected to carry out the formal concept analysis theory to mine the land-use types classification rules, and construct a heuristic classifier based on the mined classification rules. The result shows that the mined classification rules have higher credibility, and the constructed classifier has higher accuracy compared with supervision classification and C4.5 algorithm, which proves that the theory of formal concept analysis provides a new method to achieve remote sensing image classification.