With the availability of multi-sensor, multi-temporal, multi-resolution and multi-spectral images from operational Earth observation satellites, remote sensing image fusion has become a valuable tool. The goal of remote sensing image fusion is to integrate complementary information from multi-source data such that the new images are more suitable for human visual perception and computer-processing tasks such as segmentation, feature extraction, and object recognition. In this paper, a pixel-level remote sensing image fusion method is proposed, which is based on combining the principal component analysis (PCA) and the curvelet transformation (CT). First, the multi-spectral image with low-spatial-resolution is transformed by PCA and principal components are obtained. Second, the panchromatic image with high-spatial-resolution and the principal components of the multi-spectral image are respectively merged with the curvelet transform. Finally, the fused image is obtained by inverse CT and inverse PCA. The experiments using Landsat-8 OLI multi-spectral and panchromatic image show that, compared with the traditional methods such as the WT-based method, the IHS-based method, the HPF-based method, the BT-based method, the PCA-based method and the CT-based method, the results of the proposed method preserve the spatial details while preserving more spectral information of the original image.

随着来自运行中的地球观测卫星的多传感器，多时间，多分辨率和多光谱图像的可用性，遥感图像融合已成为一种有价值的工具。遥感图像融合的目标是整合来自多源数据的补充信息，以使新图像更适合于人类的视觉感知和计算机处理任务，例如分割，特征提取和目标识别。本文提出了一种基于主成分分析（PCA）和曲线波变换（CT）相结合的像素级遥感图像融合方法。首先，利用PCA对低空间分辨率的多光谱图像进行变换，得到主成分。第二，将高空间分辨率的全色图像和多光谱图像的主要成分分别与Curvelet变换合并。最后，通过逆CT和逆PCA获得融合图像。使用Landsat-8 OLI多光谱和全色图像进行的实验表明，与传统方法相比，例如基于WT的方法，基于IHS的方法，基于HPF的方法，基于BT的方法，PCA-该方法的结果既保留了空间细节，又保留了原始图像的更多光谱信息。