ANT COLONY OPTIMIZATION ALGORITHM FOR REMOTE SENSING IMAGE CLASSIFICATION USING COMBINED FEATURES

摘要：

Applying Ant Colony Optimization algorithm on the remote sensing image classification is a new research topic, and the preliminary experiments showed many promising characters, but there are also some shortcomings such as needing longer computing time and the classification accuracy is not high enough when using single feature of the image. In order to overcome these defects, we propose to combine gray feature and texture features to improve the classification rate in this paper. We also investigated the relationship between the number of ants and the classification accuracy. The experimental results prove that the improvement achieved by using combined features vector.

关键词： Ant Colony Optimization Remote Sensing Image Feature Combination Pheromone

作者: QING SONG PING GUO YUNDE JIA

作者单位: Department of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, Chin Department of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, Chin

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3478-3483

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）