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Related Experiment Video

Updated: Apr 25, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

993

Glacier segmentation algorithm that combines self-cross attention and lightweight context aggregation.

Yunzhong Shen, Xiuzai Zhang, Changjun Yang

    Applied Optics
    |April 24, 2026
    PubMed
    Summary
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    HFBFormer is a new lightweight network for glacier segmentation in challenging remote-sensing images. It achieves high accuracy and efficiency, improving glacier extraction in complex mountain environments.

    Area of Science:

    • Remote Sensing
    • Glaciology
    • Computer Vision

    Background:

    • Glacier segmentation is crucial for monitoring climate change impacts.
    • Complex optical remote-sensing scenes present significant challenges due to interference like bright backgrounds, shadows, clouds, and intricate terrain.
    • Existing methods often struggle with accuracy and efficiency in these demanding conditions.

    Purpose of the Study:

    • To develop a lightweight and robust glacier segmentation network, HFBFormer, capable of handling complex remote-sensing scenes.
    • To enhance the network's ability to model cross-scale semantic interactions and long-range dependencies.
    • To improve the refinement of glacier contours and narrow glacier tongues.

    Main Methods:

    • HFBFormer utilizes a hybrid self/cross-attention mechanism (HSC-AM) at multiple scales within an MiT-B2 encoder.

    Related Experiment Videos

    Last Updated: Apr 25, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    993
  • A feature enhancement module (FEM) combines pyramid pooling with variance-guided channel recalibration for improved feature representation.
  • A lightweight boundary sharpen module (BSM) is employed for precise contour refinement.

    • Main Results:

    • HFBFormer achieved 95.11% accuracy and 89.36% mIoU on the Animaqing glacier dataset.
    • The network demonstrates strong robustness and efficiency with only 16.37 million parameters.
    • Effective segmentation was achieved despite bright-background interference, shadow/cloud contamination, complex terrain, and blurred boundaries.

    Conclusions:

    • HFBFormer offers a highly accurate and efficient solution for glacier segmentation in challenging optical remote-sensing environments.
    • The proposed network architecture effectively addresses common difficulties in glacier extraction.
    • The results highlight HFBFormer's potential for reliable glacier monitoring and analysis.