Hyperspectral pansharpening via improved PCA approach and optimal weighted fusion strategy

Abstract

Abstract The purpose of hyperspectral pansharpening is to fuse the hyperspectral (HS) image and the panchromatic (PAN) image to generate an HS image with high spectral and spatial resolution. In this paper, a novel hyperspectral pansharpening method based on improved PCA approach and optimal weighted fusion strategy is proposed. First, the HS image is interpolated, and an improved PCA approach is proposed to obtain the spatial information of the HS image. To overcome the spectral distortion of the standard PCA method, the improved PCA approach utilizes the structural similarity index to select the appropriate component channel serving as the spatial information of the HS image. Subsequently, the PAN image is histogram matched with the selected component channel. In order to reduce the spatial distortion, an optimal weighted fusion strategy is presented to generate the adequate spatial details from the PAN and HS images. Finally, the injection gains matrix is generated to reduce the spectral distortion, and the fused HS image is obtained by injecting the extracted spatial details into the interpolated HS image. Experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in both subjective and objective evaluations.