Data augmentation-based evidential generative adversarial network for open-set hyperspectral image classification.
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| Title: | Data augmentation-based evidential generative adversarial network for open-set hyperspectral image classification. |
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| Authors: | Song, Lin1 (AUTHOR), Yang, Longpo1 (AUTHOR), Shi, Keren2 (AUTHOR), Ou, Yuan3 (AUTHOR) ouyunouyun55@163.com, Ma, Zongfang1 (AUTHOR) |
| Source: | International Journal of Remote Sensing. Feb2026, Vol. 47 Issue 4, p1809-1837. 29p. |
| Subjects: | Data augmentation, Generative adversarial networks, Classification, Artificial neural networks, Remote-sensing images, Feature extraction, Deep learning |
| Abstract: | Hyperspectral image (HSI) classification has received more and more attention due to its wide applications in the field of remote sensing. In recent years, numerous deep learning-based methods have been proposed to classify objects with known classes; however, they fail to effectively identify new samples belonging to unknown classes due to be lacking of prior knowledge about unknown categories. Although some attempts have been devoted to classify unknown samples with certain rules, less works commit to augment training data, including samples and features. Moreover, there is no work on direct classification of all classes. To solve these issues, a novel data augmentation-based evidential generative adversarial network is proposed to enhance the performance of the open-set classification of HSI. The core idea is summarized as data augmentation and direct classification. For the former, we first design an extended generator by using Kullback–Leibler divergence and bands substitution to enhance the ability of generating fake samples with authenticity and diversity. Then, feature representation is learnt by optimizing new losses based on supervised contrastive learning, which ensures the features with the same class are more clustered and the features with different classes are more scattered. In this way, it forms a clear boundary of all categories. For the latter, an evidential discriminator based on evidence deep learning is proposed to directly identify unknown objects by converting the uncertainty into the probability of the unknown classes instead of setting user-defined thresholds. Extensive experiments demonstrate that our proposed network has a competitive performance both in closed-set and open-set classification. Moreover, it outperforms the state-of-the-art open-set classification methods. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191487297 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data augmentation-based evidential generative adversarial network for open-set hyperspectral image classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Lin%22">Song, Lin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Longpo%22">Yang, Longpo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Keren%22">Shi, Keren</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ou%2C+Yuan%22">Ou, Yuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> ouyunouyun55@163.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Zongfang%22">Ma, Zongfang</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Feb2026, Vol. 47 Issue 4, p1809-1837. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Hyperspectral image (HSI) classification has received more and more attention due to its wide applications in the field of remote sensing. In recent years, numerous deep learning-based methods have been proposed to classify objects with known classes; however, they fail to effectively identify new samples belonging to unknown classes due to be lacking of prior knowledge about unknown categories. Although some attempts have been devoted to classify unknown samples with certain rules, less works commit to augment training data, including samples and features. Moreover, there is no work on direct classification of all classes. To solve these issues, a novel data augmentation-based evidential generative adversarial network is proposed to enhance the performance of the open-set classification of HSI. The core idea is summarized as data augmentation and direct classification. For the former, we first design an extended generator by using Kullback–Leibler divergence and bands substitution to enhance the ability of generating fake samples with authenticity and diversity. Then, feature representation is learnt by optimizing new losses based on supervised contrastive learning, which ensures the features with the same class are more clustered and the features with different classes are more scattered. In this way, it forms a clear boundary of all categories. For the latter, an evidential discriminator based on evidence deep learning is proposed to directly identify unknown objects by converting the uncertainty into the probability of the unknown classes instead of setting user-defined thresholds. Extensive experiments demonstrate that our proposed network has a competitive performance both in closed-set and open-set classification. Moreover, it outperforms the state-of-the-art open-set classification methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd 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: 10.1080/01431161.2026.2612897 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1809 Subjects: – SubjectFull: Data augmentation Type: general – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Classification Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Data augmentation-based evidential generative adversarial network for open-set hyperspectral image classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Lin – PersonEntity: Name: NameFull: Yang, Longpo – PersonEntity: Name: NameFull: Shi, Keren – PersonEntity: Name: NameFull: Ou, Yuan – PersonEntity: Name: NameFull: Ma, Zongfang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01431161 Numbering: – Type: volume Value: 47 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Remote Sensing Type: main |
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