A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image

摘要：

　　With the definite labeled samples,it is difficult that avoid the curse of the dimensionality for the accurate and efficient classification of hyperspectral images.It is necessary that reduce the dimensionality of hyperspectral images.Therefore,Polymorphic Ant Colony Algorithm (PACA) based band selection algorithm (PACA-BS) for hyperspectral images is proposed in this paper.Compared with the common Ant Colony Algorithm (ACA) based band selection algorithm (ACA-BS),PACA-BS can significantly decrease the searching space and thus the time complexity.These algorithms are applied to select the bands of Hyperion and AVIRIS hyperspectral image according to the class separability criterion.Performance evaluation of algorithms is focused on the following aspects: computing time and overall classification accuracy.The results showed that the computing time of PACA-BS was markedly lower than ACA-BS.Furthermore,band sets of PACA-BS generate a higher overall classification accuracy.The PACA-BS is thus proved to be a promising and optimized method for band selection of hyperspectral image.

关键词： Hyperspectral Dimensionality Reduction Band Selection Ant Colony Algorithm Polymorphic Ant Colony Algorithm

作者: Ding Xiaohui Zhang Shuqing Li Huapeng

作者单位: Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China; Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China

会议类型: 国际会议

会议名称: 2016 Internatioanl Conference on Engineering and Technology Innovations(ICETI-2016))2016年工程与技术创新国际会议

会议地点: 武汉

会议语种:英文

页码: 209-214

在线出版日期: 2016-03-25（万方平台首次上网日期，不代表论文的发表时间）