Robust Hyperspectral Unmixing With Correntropy-Based Metric
Wang, Ying; Pan, Chunhong; Xiang, Shiming; Zhu, Feiyun
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
2015-11-01
卷号24期号:11页码:4027-4040
文章类型Article
摘要Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proved to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. In addition, this task becomes more challenging in the case that the spectral bands are degraded by noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy-based metric where the nonnegative constraints on both endmembers and abundances are imposed to keep physical significance. Besides, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively reweighted nonnegative matrix factorization with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and put more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.
关键词Hyperspectral Unmixing Linear Mixture Model Non-negative Matrix Factorization Robust Estimation Correntropy Based Metric
WOS标题词Science & Technology ; Technology
DOI10.1109/TIP.2015.2456508
关键词[WOS]NONNEGATIVE MATRIX FACTORIZATION ; HALF-QUADRATIC MINIMIZATION ; CONSTRAINED LEAST-SQUARES ; ENDMEMBER EXTRACTION ; IMAGERY ; ALGORITHMS ; SIGNAL ; REPRESENTATION ; MODEL
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000359563500002
引用统计
被引频次:55[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8915
专题模式识别国家重点实验室_先进时空数据分析与学习
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
第一作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Wang, Ying,Pan, Chunhong,Xiang, Shiming,et al. Robust Hyperspectral Unmixing With Correntropy-Based Metric[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(11):4027-4040.
APA Wang, Ying,Pan, Chunhong,Xiang, Shiming,&Zhu, Feiyun.(2015).Robust Hyperspectral Unmixing With Correntropy-Based Metric.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(11),4027-4040.
MLA Wang, Ying,et al."Robust Hyperspectral Unmixing With Correntropy-Based Metric".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.11(2015):4027-4040.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
07159087.pdf(4371KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Wang, Ying]的文章
[Pan, Chunhong]的文章
[Xiang, Shiming]的文章
百度学术
百度学术中相似的文章
[Wang, Ying]的文章
[Pan, Chunhong]的文章
[Xiang, Shiming]的文章
必应学术
必应学术中相似的文章
[Wang, Ying]的文章
[Pan, Chunhong]的文章
[Xiang, Shiming]的文章
相关权益政策
暂无数据
收藏/分享
文件名: 07159087.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

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。