File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations

TitleHyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations
Authors
KeywordsSelf-similarity
Hyperspectral images (HSIs) denoising
Outlier detection
Collaborative sparsity
Low-rank representation
Issue Date2020
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2020 How to Cite?
AbstractHyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semireal and real data.
Persistent Identifierhttp://hdl.handle.net/10722/298369
ISSN
2020 Impact Factor: 5.6
2020 SCImago Journal Rankings: 2.141

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Lina-
dc.contributor.authorGao, Lianru-
dc.contributor.authorZhang, Bing-
dc.contributor.authorFu, Xiyou-
dc.contributor.authorBioucas-Dias, Jose M.-
dc.date.accessioned2021-04-08T03:08:16Z-
dc.date.available2021-04-08T03:08:16Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2020-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/298369-
dc.description.abstractHyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semireal and real data.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectSelf-similarity-
dc.subjectHyperspectral images (HSIs) denoising-
dc.subjectOutlier detection-
dc.subjectCollaborative sparsity-
dc.subjectLow-rank representation-
dc.titleHyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2020.3040221-
dc.identifier.scopuseid_2-s2.0-85097922343-
dc.identifier.eissn1558-0644-
dc.identifier.issnl0196-2892-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats