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Related Experiment Videos

Seed-to-Semantics: Few-shot Prototype-Guided Progressive Learning for Hyperspectral and LiDAR Classification.

Yiyan Zhang, Hongmin Gao, Weiping Ding

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 13, 2026
    PubMed
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    Prototype-Guided Progressive Learning (PGPL) enhances few-shot hyperspectral image (HSI) and LiDAR classification by improving pseudo-label reliability. This novel framework significantly boosts accuracy in label-scarce remote sensing scenarios.

    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Deep learning fusion of hyperspectral images (HSI) and LiDAR excels in remote sensing classification.
    • High costs of pixel-wise annotation limit its application in label-scarce settings.
    • Conventional deep models overfit, and standard semi-supervised learning (SSL) methods suffer from confirmation bias in few-shot scenarios.

    Purpose of the Study:

    • To develop a robust framework for few-shot HSI-LiDAR classification in extremely label-scarce regimes.
    • To overcome overfitting and confirmation bias issues in existing deep and semi-supervised learning methods.
    • To improve the reliability of pseudo-labels during both initialization and self-training.

    Main Methods:

    • Propose Prototype-Guided Progressive Learning (PGPL), a unified framework for few-shot HSI-LiDAR classification.

    Related Experiment Videos

  • Construct a reliable initialization pool using spectral-angle and elevation-consistency cues in the original data domain.
  • Progressively expand the training set via class-balanced pseudo-label admission and temporal confidence stabilization.

    • Main Results:

    • PGPL consistently outperforms state-of-the-art supervised and semi-supervised baselines in 2-5-shot settings.
    • Achieved accuracy gains of 4.64% (Houston), 1.16% (Trento), and 3.92% (MUUFL).
    • Demonstrated higher pseudo-label purity compared to competing methods.

    Conclusions:

    • PGPL offers a superior solution for few-shot HSI-LiDAR classification, especially in data-limited environments.
    • The framework effectively addresses challenges of overfitting and confirmation bias.
    • PGPL significantly advances the performance of multimodal remote sensing classification under extreme label scarcity.