A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter
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摘要: 遥感图像融合技术能够将两幅或多幅多源遥感图像信息进行互补、增强, 使图像携带的信息更加准确和全面。非下采样轮廓波变换(nonsubsampled contourlet transform, NSCT)对遥感数字图像进行多尺度多方向分解, 有益于提取高分遥感图像细节, 从而实现图像的锐化高空间分辨率, 但传统NSCT直接生成的高频细节信息过少, 且容易产生“虚影”现象。基于此, 论文结合NSCT与引导滤波(guided filter, GF), 提出了一种新的遥感图像全色锐化融合算法。该算法通过NSCT变换的多尺度多方向分解与重构特性, 提取直方图匹配后的图像的细节分量, 同时结合GF提取具有全色细节特征的多光谱细节分量, 最终通过加权细节信息锐化获得高空-谱融合结果。通过多个高分遥感数据集的主客观评价验证了所提出算法有效性。Abstract: Remote sensing image fusion technology can combine and enhance information from two or more multi-source remote sensing images, making the fused image more accurate and comprehensive. The nonsubsampled contourlet transform (NSCT) is effective in extracting details from high-resolution remote sensing images through multi-scale and multi-directional decomposition, thus achieving image sharpening with high spatial resolution. However, traditional NSCT produces limited high-frequency details and is prone to introduce artifacts such as “ghosting” in fused images. To address this issue, the study proposed a new panchromatic sharpening fusion algorithm for remote sensing images by combining NSCT with a guided filter (GF). Specifically, the promoted algorithm extracted the detail components from histogram-matched images using the multi-scale, multi-direction decomposition and reconstruction properties of the NSCT. Meanwhile, it extracted multi-spectral detail components with panchromatic detail features using GF. Finally, the fused images with high-spatial and high-spectral resolutions were obtained by sharpening based on weighted detail components. The proposed algorithm was proved effective through both subjective and objective evaluations using multiple high-resolution remote sensing datasets.
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