Spectral preservation fusion for remote sensing images using focus measure operators based on fast discrete curvelet transform and hyperspherical color space

Abstract

How to preserve the spectral information when enhancing the spatial details is a key issue of remote sensing image fusion. The component substitution (CS)-based fusion methods can effectively enhance the spatial details while suffering spectral distortion, and multiresolution analysis (MRA)-based methods have advantages in preserving spectral information but are not satisfactory in terms of spatial details. This paper proposes a hybrid method to integrate the advantages of CS- and MRA-based approaches. The intensity image is first obtained from an original multispectral (MS) image by hyperspherical color space (HCS) transform; then, the intensity image and original panchromatic (PAN) image are decomposed by fast discrete curvelet transform (FDCT). The focus measure operators are introduced to fused low-frequency, middle-high frequency, and fine scale subband coefficients in curvelet domain with specific fusion strategies. The final fusion image is achieved by inverse FDCT and inverse HCS transform. From the aspects of subjective and objective quality assessments, the experimental results on various types of remote sensing images including IKONOS, QuickBird, and WorldView-2 indicate that compared with existing well-known algorithms and commercial softwares, the proposed method shows obvious advantages in preserving spectral information and maintaining the spatial details.