Self-Supervised Learning Driven Cross-Domain Feature Fusion Network for Hyperspectral Image Classification

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dc.contributor.authorFang, Q.
dc.contributor.authorZhao, Y.
dc.contributor.authorWang, J.
dc.contributor.authorZhang, L.
dc.coverage.issue3cs
dc.coverage.volume34cs
dc.date.accessioned2025-07-24T12:38:41Z
dc.date.available2025-07-24T12:38:41Z
dc.date.issued2025-09cs
dc.description.abstractHyperspectral image (HSI) classification faces significant challenges due to the high cost of acquiring labeled samples. To mitigate this, we propose SSCF-Net, a novel self-supervised learning driven cross-domain feature fusion Network. SSCF-Net uniquely leverages readily available labeled natural images (source domain) to aid HSI (target domain) classification by transfer learning. Specifically, we employ rotation-based self-supervision in the source domain to learn transferable features, which are then transferred to the HSI domain. Within SSCF-Net, we effectively fuse local and global features: local features are extracted by a jointly trained module combining VGG and two-dimensional long short-term memory networks (TD-LSTM) networks, while global features capturing long-range dependencies are learned via a Transformer model. Crucially, in the HSI domain, we further employ contrastive learning as a self-supervised strategy to maximally utilize the limited labeled data. Extensive experiments on three benchmark HSI datasets (Salinas, Indian Pines, WHU-Hi-LongKou) demonstrate that SSCF-Net significantly outperforms existing methods, validating its effectiveness in addressing the label scarcity problem. The code is available at https://github.com/6pangbo/SSCF-Net.en
dc.formattextcs
dc.format.extent494-508cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2025 vol. 34, č. 3, s. 494-508. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2025.0494en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/255220
dc.language.isoencs
dc.publisherRadioengineering Societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2025/25_03_0494_0508.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectHyperspectral image classificationen
dc.subjectself-supervised learningen
dc.subjecttransfer learningen
dc.subjectfeature fusionen
dc.titleSelf-Supervised Learning Driven Cross-Domain Feature Fusion Network for Hyperspectral Image Classificationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta elektrotechniky a komunikačních technologiícs
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