QQ客服 官方微博 反馈留言

	Combining magnitude and shape features for hyperspectral classification
	Chen, Jin ; Wang, Runsheng ; Wang, Cheng ; Wang C(王程)
	2009
关键词	IMAGING SPECTROMETER DATA SUPPORT VECTOR MACHINES REMOTE-SENSING IMAGES LIKELIHOOD CLASSIFICATION SYSTEM
英文摘要	The spectral angle mapper (SAM) and maximum likelihood classification (MLC) are two traditional classifiers for hyperspectral classification. This paper presents two methods to combine magnitude and shape features, one for each classifier. As the magnitude and shape features are complementary, combining both features can improve the classification accuracy. First, magnitude features are represented by the spectral radiance vector, whereas shape features are represented by the spectral gradient vector. Then, in SAM, each feature vector generates a spectral angle for each class. The two generated angles are added together to obtain a single similarity, which is used for the final classification. Similarly, in MLC, after the dimensionality reduction using Fisher's linear discriminant (FLD), each feature vector in the new feature space generates a likelihood. The two generated likelihoods are multiplied to obtain a single value, which is adopted for the final classification. Experimental results on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set demonstrate that the proposed methods outperform the methods with a single feature set.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70972]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Chen, Jin,Wang, Runsheng,Wang, Cheng,et al. Combining magnitude and shape features for hyperspectral classification[J],2009.
APA	Chen, Jin,Wang, Runsheng,Wang, Cheng,&王程.(2009).Combining magnitude and shape features for hyperspectral classification..
MLA	Chen, Jin,et al."Combining magnitude and shape features for hyperspectral classification".(2009).

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。