结合最优亮度分量的遥感图像融合方法

针对基于亮度色调饱和度变换的遥感图像融合方法中存在的光谱损失问题，提出了一种结合最优亮度分量的融合方法．根据全色图像的亮度分量，利用克隆选择算法给出每幅单光谱图像对应的全局优化权值，该权值可反映出每幅单光谱图像相对于全色图像亮度分量中所占的折中比例，从而减弱了单光谱图像间的相关性，可获得更加逼近全色图像的亮度分量；利用最优亮度分量在改进的空间分辨率增加(ARSIS)框架下获取具有高分辨率的多光谱图像．算法针对快鸟卫星图像数据的实验结果，验证了新方法在降低光谱损失和增强融合图像细节信息方面的有效性，所获得的融合后的高分辨率多光谱图像具有较小的光谱损失．

Novel Pan-sharpening method via an optimal intensity component

The traditional Pan-sharpening method based on Intensity-Hue-Saturation transform leads to the spectral distortion problem. In this paper, a novel method is proposed for achieving the optimal intensity component to overcome this problem. According to the intensity component of the panchromatic image, the proposed method provides a global optimal weight for each single spectral image by using the Clone Selection Algorithm, which reflects the compromise proportion of each single spectral image and releases the spectrum correlation. The optimal intensity component constructed based on these weights acts actually as the panchromatic image. Using the ARSIS frame, the high-frequency information on the optimal intensity component is replaced by that of the panchromatic image to reconstruct the multi-spectral images with a high resolution by the inverse IHS. Experimental results on the data from the satellite QuickBird validate that the proposed method is effective in releasing the spectral distortion problem and enhancing the detailed information in the multi-spectral satellite images. The fused spectral images are of both high spatial resolution and low spectral distortion.