版权所有:内蒙古大学图书馆 技术提供:维普资讯• 智图
内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Provinc Peoples R China Northumbria Univ Dept Comp Sci & Digital Technol Newcastle Upon Tyne Tyne & Wear England Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Sydney NSW Australia
出 版 物:《INTERNATIONAL JOURNAL OF REMOTE SENSING》 (国际遥感杂志)
年 卷 期:2016年第37卷第19期
页 面:4501-4520页
核心收录:
学科分类:083002[工学-环境工程] 0830[工学-环境科学与工程(可授工学、理学、农学学位)] 1002[医学-临床医学] 08[工学] 09[农学] 0804[工学-仪器科学与技术] 0903[农学-农业资源与环境] 0816[工学-测绘科学与技术] 081602[工学-摄影测量与遥感] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程]
基 金:National Natural Science Foundation of China
主 题:HYPERSPECTRAL imaging systems ALGORITHMS -- Evaluation BEAMFORMING PIXEL density measurement BIG data
摘 要:Spatial information has been widely used for hyperspectral image classification, which can dramatically improve the classification accuracy. Though band selection is an important preprocessing step for hyperspectral image processing, spatial information has not been well exploited in this field. In this article, we will exploit the spatial information for band selection. This article mainly includes two parts: algorithm design, and algorithm evaluation. In the first part, we propose an efficient band selection method by using the spatial structure information and spectral information. In the second part, we advocate the use of the local spatial filtering and the spectral-spatial classifier for evaluating the performance of band selection algorithms instead of the traditional pixel-wise classifiers. Comprehensive experiments over diverse publicly available benchmark data sets reveal some interesting results.