关键词: 蚁群优化算法, 分类规则挖掘, 遥感图像分类

Abstract: Ant Colony Optimization（ACO） as one of the main algorithms in swarm intelligence has been applied successfully to optimization problems in many research areas，but it is still a new research topic in remote sensing data processing.The ACO algorithm has self-organization，cooperation，communication，and other intelligent merits，and without prior knowledge of statistical distribution parameters，so it has many potential advantages in the remote sensing data processing research area.This paper introduces the theory and algorithm process of the ACO for mining classification rules applied to remote sensing image classification.Selecting CBERS image located in Beijing area as experimental data，this paper use the ACO based rule mining algorithm for constructing the classification rules，then use these rules to classify the example data，and compared the classification result with Maximum Likelihood Classification（MLC） method，the experiment results show that ACO based classification rule mining algorithm has provided a new method for remote sensing image classification.

Key words: Ant Colony Optimization（ACO） algorithm, classification rule mining, remote sensing image classification