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, Wenxiang; Zhang, Lifu; Ren, Jinchang

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 fully learn task-related and domain-confusing features, a convolutional neural network (CNN) and Transformer-based multilevel features extractor (generator) is developed to enrich the feature representation of two domains. Furthermore, to alleviate the harm even the negative transfer to UDA caused by task-irrelevant features, a task-oriented feature decomposition method is leveraged to enhance the task-related features while suppressing task-irrelevant features, and enabling the aligned domain-invariant features can be contributed to the classification task explicitly. Extensive experiments on three cross-scene HSI benchmarks have validated the effectiveness of the proposed framework.

Citation

ZHAO, C., QIN, B., FENG, S., ZHU, W., ZHANG, L. and REN, J. 2022. An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 60, article 5546216. Available from: https://doi.org/10.1109/TGRS.2022.3230378

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2022
• Online Publication Date: Dec 16, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Jan 12, 2023
• Publicly Available Date: Jan 12, 2023
• Journal: IEEE transactions on geoscience and remote sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 60
• Article Number: 5546216
• DOI: https://doi.org/10.1109/TGRS.2022.3230378
• Keywords: Cross-scene classification; Hyperspectral image (HSI); Task irrelevant; Task specific; Unsupervised domain adaptation (UDA)
• Public URL: https://rgu-repository.worktribe.com/output/1843977