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<p>The acquisition of remote sensing images with both high spatial resolution and high spectral resolution is constrained by limitations in spectral imaging technology. Pan-sharpening is utilized to generate high-resolution multispectral images that correspond to the resolution of panchromatic (PAN) images through the fusion of multispectral (MS) and PAN images. Traditional methods frequently encounter challenges associated with the loss of image details. This study introduces a novel multispectral image fusion method based on Side Window Filtering (SWF) and a new injected gain, termed the improved adaptive Gram-Schmidt method based on SWF (SWGSA), with an emphasis on detail preservation. Initially, side window filtering is applied to the PAN image, which not only reduces noise but also enhances edge retention, leading to a more accurate computation of weight indices. Subsequently, the injected gain is adjusted by incorporating references from the PAN image to further enhance image details. Ultimately, the fused image is produced through this process. Experimental results from the IKONOS, GeoEye-1, and WorldView-3 datasets substantiate the effectiveness of the proposed method, demonstrating a significant improvement in the quality of the fused images.</p>

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A novel pansharpening method based on side window filter and new injection gain matrices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月18日

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  • Tianci Liu,
  • Keyan Dong,
  • Yansong Song,
  • Jinwang Li,
  • Junyao Wang,
  • Yanbo Wang,
  • Lei Zhang,
  • Yuqing Li

摘要

The acquisition of remote sensing images with both high spatial resolution and high spectral resolution is constrained by limitations in spectral imaging technology. Pan-sharpening is utilized to generate high-resolution multispectral images that correspond to the resolution of panchromatic (PAN) images through the fusion of multispectral (MS) and PAN images. Traditional methods frequently encounter challenges associated with the loss of image details. This study introduces a novel multispectral image fusion method based on Side Window Filtering (SWF) and a new injected gain, termed the improved adaptive Gram-Schmidt method based on SWF (SWGSA), with an emphasis on detail preservation. Initially, side window filtering is applied to the PAN image, which not only reduces noise but also enhances edge retention, leading to a more accurate computation of weight indices. Subsequently, the injected gain is adjusted by incorporating references from the PAN image to further enhance image details. Ultimately, the fused image is produced through this process. Experimental results from the IKONOS, GeoEye-1, and WorldView-3 datasets substantiate the effectiveness of the proposed method, demonstrating a significant improvement in the quality of the fused images.