Pan-Sharpening Framework Based on Multiscale Entropy Level Matching and Its Application

Current remote sensing hardware technology is not yet able to acquire multiband remote sensing images with both high spatial and spectral resolution. As an important tool to compensate for the lack of spatial information acquisition of multispectral (MS) images, pan-sharpening has been an important and continuously active research area in remote sensing image processing. Although many methods have emerged, the problem of how to obtain high spatial resolution while effectively maintaining the spectral information of MS images has not been well solved. Many aspects still need further research. In this article, we first investigate the essential properties and rationality of two common framework types in the multiresolution analysis (MRA) sharpening method of pan-sharpening from the source perspective—the identical-resolution framework (IRF) derived from the generalized fusion application and the different-resolution framework (DRF) exclusive to the sharpening application, and show that the core difference between the two frameworks lies in the different ideas of utilizing the multiscale transformation, i.e., they tend to expand the scale space and model the spatially blurred degradation relationship between the sources, respectively. Both of them have their own advantages and disadvantages in handling detailed information, and neither of them can effectively deal with the "detail exclusivity" problem. Based on this, the idea of "entropy level matching" (ELM) of pan-sharpening is presented, and a comprehensive framework that can combine the advantages of the two types of frameworks is constructed, namely, the multiscale ELM framework. Furthermore, as an application of this framework, we propose a sharpening method shearlet transform-based entropy matching (STEM) built on the nonsubsampled shearlet as a multiscale transformation method. According to the difference in detail injection mode in it, it can be further divided into two sharpening methods based on additive mode and substitutive mode. The comparison experiments with 11 popular methods show that the proposed two sharpening methods can effectively improve the spatial resolution of MS images while keeping the spectral information well, and the comprehensive performance advantage is obvious. The source code of the proposed method can be downloaded from https://github.com/JZ-Tao/STEM/ .