QQ客服 官方微博 反馈留言

	Optimal band selection for high dimensional remote sensing data using genetic algorithm
	Zhang, Xianfeng ; Sun, Quan ; Li, Jonathan
	2009
英文摘要	A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0 representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing, especially in hyperspectral dimensionality reduction. ? 2009 SPIE.; EI; 0
语种	英语
DOI标识	10.1117/12.847907
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/312367]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Zhang, Xianfeng,Sun, Quan,Li, Jonathan. Optimal band selection for high dimensional remote sensing data using genetic algorithm. 2009-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。