An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification

Zhao, Chunhui; Qin, Boao; Feng, Shou; Zhu, Wen‐Xiang; Zhang, Lifu; Ren, Jinchang

doi:10.1109/tgrs.2022.3230378

articleIEEE Transactions on Geoscience and Remote SensingJan 1, 2022GREEN OA

An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification

CZChunhui Zhao BQBoao Qin SFShou Feng WZWen‐Xiang Zhu LZLifu Zhang

Harbin Engineering University · Ministry of Industry and Information Technology · +4 more institutions

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Abstract

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performance degradation. To tackle this issue, a novel unsupervised domain adaptation (UDA) framework toward multilevel features and decision boundaries (ToMF-B) is proposed for the cross-scene HSIC, which can align task-related features and learn task-specific decision boundaries in parallel. Based on the maximum classifier discrepancy, a two-stage alignment scheme is proposed to bridge the interdomain gap and generate discriminative decision boundaries. In addition, to…

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Topics & keywords

Topics

Keywords

Hyperspectral imaging
Computer science
Artificial intelligence
Pattern recognition (psychology)
Contextual image classification
Domain adaptation
Image (mathematics)
Adaptation (eye)

UN Sustainable Development Goals

Reduced inequalities

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