.png){kind=icon}
返回
A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
.png){kind=icon}
OA
AI
DOI：10.1109/LGRS.2018.2878394.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent the limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can greatly benefit from spatial regularization strategies. However, existing spatial regularization strategies lead to large-scale non-smooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge and a necessity for many real world applications. In this letter, a novel multiscale spatial regularization approach for sparse unmixing is proposed. The method uses a signal-adaptive spatial multiscale decomposition based on segmentation and oversegmentation algorithms to decompose the unmixing problem into two simpler problems: one in an approximation image domain and another in the original domain. Simulation results using both synthetic and real data indicate that the proposed method outperforms the state-of-the-art total variation-based algorithms with a computation time comparable to that of their unregularized counterparts.
Keyword:
Hyperspectral data
multiscale
sparse unmixing
spatial regularization
superpixels
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
.png){kind=icon}
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images
OAAI
OAAI分享收藏Spectral-Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing用于高光谱图像分解的光谱空间加权稀疏回归
PREAI
分享收藏Health Literacy – a review of research using the European Health Literacy Questionnaire (HLS-EU-Q16) in 2010-2018健康素养-2010-2018使用欧洲健康素养问卷 (HLS-EU-Q16) 进行的研究综述OAAI
OAAI分享收藏分享收藏
查看更多内容