Pansharpening Based on Joint-Guided Detail Extraction

Abstract

Pansharpening is the process of fusing low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images, so as to obtain high spatial resolution multispectral (HRMS) images. In this article, a new pansharpening method based on a joint-guided detail extraction is proposed to maintain the spectral and spatial fidelity of a pansharpened image. First, to obtain details that are highly correlated with an MS image, a new PAN image is constructed and guided by the intensity component of the MS image through a variational model. The construction of the new PAN image improves the correlation between the PAN and MS images, and thus reduces the spectral distortion. The variational model is rapidly solved using the least-squares method. Second, to obtain accurate details from the new PAN image, the extraction of the details is guided by each band of the MS image through a regression model, which can further reduce the spatial distortion. The regression model is effectively solved using the gradient descend method. Finally, the details are injected into the upsampled MS image to obtain a fused image. Numerous experiments on the proposed approach were conducted and the results were compared with previous state-of-the-art pansharpening methods. The experimental results verify that the proposed method can efficiently achieve high-quality HRMS images.

Keywords

• Detail extraction
• joint-guided
• pansharpening
• regression model
• variational model