Context adaptive panchromatic band simulation and detail injection for image pansharpening

Abstract

General component substitution (CS) pansharpening methods establish a global model over the whole image plane and may lead to unexpected spectral distortion. This paper proposes a context adaptive CS pansharpening method, which features a totally local-based processing procedure. The method consists of two processing blocks. The first block is to simulate a low-resolution panchromatic band by a local linear regression model between panchromatic and multispectral bands. The second block extracts spatial details and adds details back to multispectral bands in locally varying ratios. By recasting the local linear regression model into the guided filtering framework and analyzing the implicit statistical assumptions underlying CS methods, the strengths of the local-based pansharpening algorithm are addressed. Experiments test 7 pairs of images acquired from different sensors, such as GF-2, Quickbird, and Worldview-2. Both quantitative and qualitative evaluations reveal that the presented method can better preserve the spectral information than some state-of-the-art methods.