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Conference Paper: 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
KeywordsHSI denoising
Collaborative sparsity
Outlier detection
Self-similarity
Low-rank representation
Issue Date2017
Citation
Conference on Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland, 11-14 September 2017. In Proceedings of SPIE - The International Society for Optical Engineering, 2017, v. 10427, article no. 104270M How to Cite?
AbstractThe very high spectral resolution of Hyperspectral Images (HSIs) enables the identification of materials with subtle differences and the extraction subpixel information. However, the increasing of spectral resolution often implies an increasing in the noise linked with the image formation process. This degradation mechanism limits the quality of extracted information and its potential applications. Since HSIs represent natural scenes and their spectral channels are highly correlated, they are characterized by a high level of self-similarity and are well approximated by low-rank representations. These characteristic underlies the state-of-The-Art in HSI denoising. However, in presence of rare pixels, the denoising performance of those methods is not optimal and, in addition, it may compromise the future detection of those pixels. To address these hurdles, 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 semi-real data.
Persistent Identifierhttp://hdl.handle.net/10722/298246
ISSN
2020 SCImago Journal Rankings: 0.192

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Lina-
dc.contributor.authorGao, Lianru-
dc.contributor.authorZhang, Bing-
dc.contributor.authorBioucas-Dias, Jose M.-
dc.date.accessioned2021-04-08T03:08:00Z-
dc.date.available2021-04-08T03:08:00Z-
dc.date.issued2017-
dc.identifier.citationConference on Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland, 11-14 September 2017. In Proceedings of SPIE - The International Society for Optical Engineering, 2017, v. 10427, article no. 104270M-
dc.identifier.issn0277-786X-
dc.identifier.urihttp://hdl.handle.net/10722/298246-
dc.description.abstractThe very high spectral resolution of Hyperspectral Images (HSIs) enables the identification of materials with subtle differences and the extraction subpixel information. However, the increasing of spectral resolution often implies an increasing in the noise linked with the image formation process. This degradation mechanism limits the quality of extracted information and its potential applications. Since HSIs represent natural scenes and their spectral channels are highly correlated, they are characterized by a high level of self-similarity and are well approximated by low-rank representations. These characteristic underlies the state-of-The-Art in HSI denoising. However, in presence of rare pixels, the denoising performance of those methods is not optimal and, in addition, it may compromise the future detection of those pixels. To address these hurdles, 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 semi-real data.-
dc.languageeng-
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering-
dc.subjectHSI denoising-
dc.subjectCollaborative sparsity-
dc.subjectOutlier detection-
dc.subjectSelf-similarity-
dc.subjectLow-rank representation-
dc.titleHyperspectral image denoising and anomaly detection based on low-rank and sparse representations-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.2280456-
dc.identifier.scopuseid_2-s2.0-85041040580-
dc.identifier.volume10427-
dc.identifier.spagearticle no. 104270M-
dc.identifier.epagearticle no. 104270M-
dc.identifier.eissn1996-756X-
dc.identifier.issnl0277-786X-

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