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MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION

机译:基于脉冲耦合神经网络分段的多光谱全景投影方法

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The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentation rather than through a simple square window. Since PCNN segmentation agrees with the human visual system, the proposed method shows better spectral consistency. Our experiments, which have been carried out for both suburban and urban datasets, demonstrate that the proposed method outperforms other methods in multispectral pansharpening.
机译:提出了一种基于脉冲耦合神经网络分割的泛锐化方法。在新方法中,通过PCNN分割而不是通过简单的正方形窗口来估计每个区域的均匀注入增益。由于PCNN分割与人类视觉系统一致,因此所提出的方法显示出更好的光谱一致性。我们针对郊区和城市数据集进行的实验表明,该方法在多光谱全景锐化方面优于其他方法。

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