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Cross-Domain Few-Shot Learning Method Based on Fractional Domain Information for Hyperspectral Image Multi-Class Change Detection

Shou Feng,Jinghe Zhang,Yuanze Fan,Xinyao Liu,Chunhui Zhao,Wei Li,Ran Tao

Published 2025 in IEEE transactions on circuits and systems for video technology (Print)

ABSTRACT

Hyperspectral image multi-class change detection (HSI-MCD) based on deep learning (DL) rely significantly on the number of labeled data. Due to the high cost of manually labeling for hyperspectral images (HSIs), obtaining a large amount of labeled samples is difficult. Moreover, for multi-class change detection (MCD) tasks, there is the phenomenon of semantic cross-coupling of changes due to complex change scenarios. To solve the above problems, a cross-domain few-shot learning method based on fractional domain information for HSI-MCD (FrCFSL) is proposed. Firstly, a spectral-spatial-fractional information extraction module is proposed, which can extract spectral-spatial-fractional domain joint feature. Thus, the module can obtain more comprehensive and discriminative representations of land cover categories, alleviating the phenomenon of semantic cross-coupling between classes. Afterward, a cross-domain few-shot learning strategy is introduced, where it learns task-relevant category discrimination meta-knowledge from a pair of richly labeled very high-resolution optical images (VHRIs) dataset and transfers it to the bitemporal HSIs dataset. Thus, the model can achieve better MCD performance with a small number of labeled samples. Finally, to mitigate the domain distribution differences between VHRIs data and HSIs data, a topological structure alignment module is proposed to align the intrinsic topological relationships between land cover categories, thus narrowing the gap between the two domain distributions. Through experiments conducted on three HSI-MCD datasets and comparative analysis with six state-of-the-art methods, the validity and stability of the proposed method are indicated.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE transactions on circuits and systems for video technology (Print)

  • Publication date

    2025-12-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TCSVT.2025.3570466
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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