Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising
文献类型：期刊论文
| 作者 | Yuan, Yuan ; Zheng, Xiangtao; Lu, Xiaoqiang
|
| 刊名 | ieee transactions on geoscience and remote sensing
 |
| 出版日期 | 2015-07-01 |
| 卷号 | 53期号:7页码:3815-3832 |
| 关键词 | Adaptive kernel hyperspectral image (HSI) denoising nonlocal means (NLM) spectral-spatial kernel regularization |
| 英文摘要 | noise contamination is a ubiquitous problem in hyperspectral images (hsis), which is a challenging and promising theme in many remote sensing applications. a large number of methods have been proposed to remove noise. unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in hsis. recently, a spectral-spatial adaptive hyperspectral total variation (ssahtv) was proposed and obtained promising results. however, the ssahtv model is insensitive to the image details, which makes the edges blur. to overcome all of these drawbacks, a spectral-spatial kernel method for hsi denoising is proposed in this paper. the proposed method is inspired by the observation that the spectral-spatial information is highly redundant in hsis, which is sufficient to estimate the clear images. in this paper, a spectral-spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. the reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data. |
| WOS标题词 | science & technology ; physical sciences ; technology |
| 类目[WOS] | geochemistry & geophysics ; engineering, electrical & electronic ; remote sensing ; imaging science & photographic technology |
| 研究领域[WOS] | geochemistry & geophysics ; engineering ; remote sensing ; imaging science & photographic technology |
| 关键词[WOS] | principal component analysis ; noise-reduction ; dimensionality reduction ; nonlocal regularization ; sparse representation ; quality assessment ; algorithms ; selection ; shrinking ; removal |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000351461000021 |
| 公开日期 | 2015-07-28 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/24117]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuan, Yuan,Zheng, Xiangtao,Lu, Xiaoqiang. Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising[J]. ieee transactions on geoscience and remote sensing,2015,53(7):3815-3832. |
| APA | Yuan, Yuan,Zheng, Xiangtao,&Lu, Xiaoqiang.(2015).Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising.ieee transactions on geoscience and remote sensing,53(7),3815-3832. |
| MLA | Yuan, Yuan,et al."Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising".ieee transactions on geoscience and remote sensing 53.7(2015):3815-3832. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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