Knowledge Commons of Institute of Automation,CAS
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 |
DOI | 10.1109/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/8915 |
专题 | 多模态人工智能系统全国重点实验室_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. |
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