A pixel-level fusion method for multi-source optical remote sensing image combining the principal component analysis and curvelet transform

Abstract

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.