四川大学学报（自然科学版）2017，Vol.54Issue(2)：303-308,6.DOI:103969/j.issn.0490-6756.2017.03.014
噪声抑制的高光谱图像虚拟维数分析
Virtual dimensionality analysis of hyperspectral imagery with noise being constrained
摘要
Abstract
In dimensionality reduction process of hyperspectral data,intrinsic dimension is normally characterized by virtual dimension.Classic algorithm mainly uses hypothesis-testing criterion to set eigenvalue threshold and correspondingly obtains virtual dimension.But under strong noises,it may not estimate very well.A noise constrained virtual dimension (NCVD) analysis method of hyperspectral imagery is proposed in this paper.It decreases the computational complexity by the QR decomposing;improves the accuracy of the estimated dimension by adopting sliding noise detection window to filter the noise;synthesizes the least squares algorithm to modify threshold for reasonable results.The experimental results prove the feasibility and superiority of the proposed algorithm by using simulated and real data.关键词
高光谱图像/虚拟维数/QR分解/滑动噪声检测Key words
Hyperspectral imagery/Virtual dimension/QR decomposing/Sliding noise detection分类
信息技术与安全科学引用本文复制引用
何秉钧,蒋鸣飞,罗欣,王蓉..噪声抑制的高光谱图像虚拟维数分析[J].四川大学学报（自然科学版）,2017,54(2):303-308,6.基金项目
国家973计划项目(2013CB733400) （2013CB733400）
中央高校基本科研业务费项目(ZYGX2013J120) （ZYGX2013J120）
电子科技大学本科教育教学研究项目(2015XJYYB088) （2015XJYYB088）
