Tensor-Based Classification Models for Hyperspectral Data Analysis.
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| Title: | Tensor-Based Classification Models for Hyperspectral Data Analysis. |
|---|---|
| Authors: | Makantasis, Konstantinos, Doulamis, Anastasios D., Doulamis, Nikolaos D., Nikitakis, Antonis |
| Source: | IEEE Transactions on Geoscience & Remote Sensing. Dec2018, Vol. 56 Issue 12, p6884-6898. 15p. |
| Subjects: | Hyperspectral imaging systems, Tensor algebra, Feedforward neural networks, Support vector machines, Logistic regression analysis, Machine learning |
| Abstract: | In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting the principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and nonlinear classifiers. The advantages of the proposed classification approach are that: 1) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples); 2) it provides a physical interpretation of model coefficients on the classification output; and 3) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits the principles of logistic regression, assuming the rank-1 canonical decomposition property among its weights. For the nonlinear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state-of-the-art classification methods, either linear (e.g., linear support vector machine) or nonlinear (e.g., deep learning), indicate the outperformance of the proposed scheme, especially in the cases where a small number of training samples is available. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Geoscience & Remote Sensing is the property of IEEE 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: 133667634 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tensor-Based Classification Models for Hyperspectral Data Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Makantasis%2C+Konstantinos%22">Makantasis, Konstantinos</searchLink><br /><searchLink fieldCode="AR" term="%22Doulamis%2C+Anastasios+D%2E%22">Doulamis, Anastasios D.</searchLink><br /><searchLink fieldCode="AR" term="%22Doulamis%2C+Nikolaos+D%2E%22">Doulamis, Nikolaos D.</searchLink><br /><searchLink fieldCode="AR" term="%22Nikitakis%2C+Antonis%22">Nikitakis, Antonis</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Geoscience+%26+Remote+Sensing%22">IEEE Transactions on Geoscience & Remote Sensing</searchLink>. Dec2018, Vol. 56 Issue 12, p6884-6898. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Tensor+algebra%22">Tensor algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting the principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and nonlinear classifiers. The advantages of the proposed classification approach are that: 1) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples); 2) it provides a physical interpretation of model coefficients on the classification output; and 3) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits the principles of logistic regression, assuming the rank-1 canonical decomposition property among its weights. For the nonlinear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state-of-the-art classification methods, either linear (e.g., linear support vector machine) or nonlinear (e.g., deep learning), indicate the outperformance of the proposed scheme, especially in the cases where a small number of training samples is available. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Geoscience & Remote Sensing is the property of IEEE 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: Identifiers: – Type: doi Value: 10.1109/TGRS.2018.2845450 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 6884 Subjects: – SubjectFull: Hyperspectral imaging systems Type: general – SubjectFull: Tensor algebra Type: general – SubjectFull: Feedforward neural networks Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Tensor-Based Classification Models for Hyperspectral Data Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Makantasis, Konstantinos – PersonEntity: Name: NameFull: Doulamis, Anastasios D. – PersonEntity: Name: NameFull: Doulamis, Nikolaos D. – PersonEntity: Name: NameFull: Nikitakis, Antonis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 01962892 Numbering: – Type: volume Value: 56 – Type: issue Value: 12 Titles: – TitleFull: IEEE Transactions on Geoscience & Remote Sensing Type: main |
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