Pan-Sharpening by Multilevel Interband Structure Modeling

Abstract

Pan-sharpening is designed to estimate multi-/hyperspectral (MS/HS) images that would have been observed with a sensor at higher resolution. It is a very important issue for many remote sensing and mapping applications. This letter proposes an improved pan-sharpening algorithm based on the ARSIS concept under the assumption that missing information of a low-resolution MS/HS image is linked to the high frequencies of MS/HS and panchromatic (PAN) images. The main object of this letter is to exhibit a multilevel interband structure model that better considers the inherent relationship between the hierarchical structures of MS/HS and PAN images. Several groups of data sets are used to demonstrate the performance of the proposed method. The results show that the proposed method outperforms the existing ARSIS-based and some other fusion techniques and can be extended to HS image sharpening as well.