Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods

Abstract

In many applications, it is imperative to maintain high spectral and spatial resolution of remote sensing images. This letter addresses the issue by fusing low-spatial-resolution hyperspectral images (HSIs) and high-spatial-resolution multispectral images (MSIs) of the same scene collected by the coupled sensors and, thus, present a novel framework that generalizes well-established pan-sharpening algorithms. The main steps of the framework are dividing the spectrum of HSIs into several regions and fusing HSIs and MSIs in each region by the chosen pan-sharpening algorithm. Ratio image-based spectral resampling (RIBSR) is used to interpolate the missing data so that every region is covered by a multispectral band. Therefore, the framework allows most of pan-sharpening algorithms to be extended to HSI and MSI fusion. Synthetic data in accordance with sensor reality are used to test specific methods derived within the framework. Experimental results show that the proposed methods excel the state-of-the-art methods in terms of simplicity, feasibility, efficiency, and effectiveness.