An Unsupervised Classification Method of Remote Sensing Images Based on Ant Colony Optimization Algorithm

摘要：

Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning procedure for deducing a function from training data;unsupervised classification is a kind of classification which no training sample is available and subdivision of the feature space is achieved by identifying natural groupings present in the images values. As a branch of swarm intelligence, ant colony optimization algorithm has self-organization, adaptation, positive feedback and other intelligent advantages. In this paper, ant colony optimization algorithm is tentatively introduced into unsupervised classification of remote sensing images. A series of experiments are performed with remote sensing data. Comparing with the K-mean and the ISODATA clustering algorithm, the experiment result proves that artificial ant colony optimization algorithm provides a more effective approach to remote sensing images classification.

关键词： unsupervised classification pheromone data discretization ant colony optimization algorithm

作者: Duo Wang Bo Cheng

作者单位: Center for Earth Observation and Digital Earth Chinese Academy of Sciences China Graduate University Center for Earth Observation and Digital Earth Chinese Academy of Sciences China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 294-301

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）