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October 12, 2025Applied SciencesOpen Access

MKF-NET: KAN-Enhanced Vision Transformer for Remote Sensing Image Segmentation

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MKF-NET achieved a pixel precision of 78.53%, improving segmentation accuracy significantly.
The model's innovative fusion of KAN convolution and Vision Transformer enhances small object recognition.
Experiments on the LoveDA dataset demonstrated MKF-NET outperformed traditional deep learning models.
The research provides valuable insights for applications in environmental monitoring and disaster response.

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Deep learning model improves semantic segmentation in remote sensing images, highlighting KAN convolution and ViT.

Ye et al. (2025) studied this question.

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