Improved Hyperspectral Imagery Classification with Adaptive Denoising.
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| Title: | Improved Hyperspectral Imagery Classification with Adaptive Denoising. |
|---|---|
| Authors: | Chen, Guang Yi1 guang_c@cse.concordia.ca, Sun, Wei2 wei.sun@concordia.ca, Qian, Shen-En3 shen-en.qian@asc-csa.gc.ca |
| Source: | Engineering Letters. Jul2026, Vol. 34 Issue 7, p2710-2715. 6p. |
| Subjects: | Image denoising, Principal components analysis, Hyperspectral imaging systems, Feature extraction, Signal denoising, Image recognition (Computer vision), Noise control |
| Abstract: | In today's hyperspectral imagery (HSI) data cubes, there always exists a small amount of noise no matter how good the imaging sensors are. As a result, there is a need to reduce this amount of noise to improve classification accuracy. In this paper, we propose to improve hyperspectral imagery classification by introducing denoising adaptively. We do not add noise to the HSI data cubes but reduce noise from them by means of principal component analysis (PCA) preprocessing. We reduce noise for the noisy PCA output bands by means of video block matching and 3D filtering and then perform inverse PCA to obtain denoised data cubes. We combine this denoising technique with segmented PCA and the Gaussian pyramid decomposition-based multiscale feature fusion. Experiments demonstrate that the classification accuracies for the proposed method improve considerably for three well-known HSI data cubes. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088774 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improved Hyperspectral Imagery Classification with Adaptive Denoising. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Guang+Yi%22">Chen, Guang Yi</searchLink><relatesTo>1</relatesTo><i> guang_c@cse.concordia.ca</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Wei%22">Sun, Wei</searchLink><relatesTo>2</relatesTo><i> wei.sun@concordia.ca</i><br /><searchLink fieldCode="AR" term="%22Qian%2C+Shen-En%22">Qian, Shen-En</searchLink><relatesTo>3</relatesTo><i> shen-en.qian@asc-csa.gc.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jul2026, Vol. 34 Issue 7, p2710-2715. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+denoising%22">Image denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+control%22">Noise control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In today's hyperspectral imagery (HSI) data cubes, there always exists a small amount of noise no matter how good the imaging sensors are. As a result, there is a need to reduce this amount of noise to improve classification accuracy. In this paper, we propose to improve hyperspectral imagery classification by introducing denoising adaptively. We do not add noise to the HSI data cubes but reduce noise from them by means of principal component analysis (PCA) preprocessing. We reduce noise for the noisy PCA output bands by means of video block matching and 3D filtering and then perform inverse PCA to obtain denoised data cubes. We combine this denoising technique with segmented PCA and the Gaussian pyramid decomposition-based multiscale feature fusion. Experiments demonstrate that the classification accuracies for the proposed method improve considerably for three well-known HSI data cubes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 2710 Subjects: – SubjectFull: Image denoising Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Hyperspectral imaging systems Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Signal denoising Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Noise control Type: general Titles: – TitleFull: Improved Hyperspectral Imagery Classification with Adaptive Denoising. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Guang Yi – PersonEntity: Name: NameFull: Sun, Wei – PersonEntity: Name: NameFull: Qian, Shen-En IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 7 Titles: – TitleFull: Engineering Letters Type: main |
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