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Novel frequency-adaptive methods enhance remote sensing image processing

Novel frequency-adaptive methods enhance remote sensing image processing
The overall structure of FAME-Net. Credit: Zhang Jie

Researchers led by Prof. Xie Chengjun and Associate Prof. Zhang Jie at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences have developed a novel deep learning-based method for satellite imagery.

Their method, called the Frequency-Adaptive Mixture of Experts Network (FAME-Net), has been accepted for publication in the 2024 Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI). It is posted to the arXiv preprint server.

High-resolution multispectral imagery is essential for agriculture, mapping, and . However, direct acquisition of such images faces technological limitations. To overcome this challenge, pan-sharpening techniques combine high-resolution panchromatic (PAN) and low-resolution multispectral images. Recent advances in have improved spectral and spatial detail in pan-sharpening, but still struggle with frequency bias and adaptation to diverse remote sensing content.

Based on the discrete cosine transform and the Mixture of Expert concepts, the researchers proposed the FAME-Net, which uses a frequency mask predictor for adaptive high- and low-frequency masking.

Different expert networks process these frequency-specific features, allowing focused attention on different frequency ranges. FAME-Net dynamically adapts its masks to different image contents by integrating multiple expert outputs through a gating mechanism.

Novel frequency-adaptive methods enhance remote sensing image processing
The result of FAME-Net was compared against nine other methods on WorldView-III dataset. Credit: Zhang Jie

In comparative analyses with existing state-of-the-art methods, FAME-Net not only demonstrates superior performance in preserving spectral quality and enhancing but also shows excellent performance in full-resolution remote sensing imagery.

This study provides new insights into the field of image processing and demonstrates the effectiveness of integrating dynamic network structures and frequency domain information.

More information: Xuanhua He et al, Frequency-Adaptive Pan-Sharpening with Mixture of Experts, arXiv (2024). DOI: 10.48550/arxiv.2401.02151

Journal information: arXiv
Citation: Novel frequency-adaptive methods enhance remote sensing image processing (2024, January 18) retrieved 15 April 2024 from
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