遥感影像融合AIHS转换与粒子群优化算法

Pan-sharpening是通过将低分辨率多光谱图像(LMS)与高分辨率全色图像(PAN)进行合成而获得高光谱高空间分辨率的多光谱图像(HMS)的过程。本文提出一种Pan-sharpening方法,称为PAIHS。该方法基于自适应亮度-色度-饱和度(AIHS)转换和变分Pan-sharpening框架以及两个假设(①Pan-sharpening图像和原始多光谱图像(MS)具有相同的光谱信息;②Pan-sharpening图像与全色图像(PAN)包含的几何信息保持一致),同时确定目标函数,然后用粒子群算法(PSO)进行优化,目的是得到最佳控制参数并求得目标函数最小值,此时对应着最好的Pan-sharpening质量。试验结果表明,本文提出的方法具有高效性和可靠性,获得的性能指标也优于目前一些主流的融合方法。

Joint AIHS and particle swarm optimization for Pan-sharpening

Pan-sharpening is a process of obtaining a high spatial and spectral multispectral image (HMS) by combining a low resolution multispectral image (LMS) with a high resolution panchromatic image (PAN). In this paper, a Pan-sharpening method called PAIHS is proposed. It is based on adaptive intensity-hue-saturation (AIHS) transformation, variational Pan-sharpening framework and two assumptions:①pan-sharpened image and original multispectral image (MS) have the same spectral information; ②pan-sharpened image and PAN image contain the same geometric information. The suitable objective function was established, and optimized by particle swarm optimization (PSO) to obtain the optimal control parameters and minimum value, which corresponds to the best Pan-sharpening quality. The experimental results show that the proposed method has high efficiency and reliability, and the obtained performance index is also better than some of the current mainstream fusion methods.