Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2022.3230378
Keywords
Cross-scene classification; hyperspectral image (HSI); task irrelevant; task specific; unsupervised domain adaptation (UDA)
Categories
Funding
- National Natural Science Foundation of China [62002083, 61971153]
- Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS202210]
- Heilongjiang Provincial Natural Science Foundation [LH2021F012]
- Fundamental Research Funds for the Central Universities [3072021CFT0801, 3072022QBZ0805, 3072022CF0808]
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In this paper, a novel unsupervised domain adaptation framework is proposed for cross-scene hyperspectral image classification. The framework aligns task-related features and learns task-specific decision boundaries, improving the classification performance.
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.
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