Infrared remote-sensing image super-resolution based on physical characteristic deduction

Abstract

In this paper, we propose a high-confidence, super-resolution method for short-wave infrared remote-sensing images based on characteristic deduction form high-resolution visible images. First, based on the radiative transmission mechanism of full remote-sensing imaging link, a ground object characterization model based on the dark object method was established to achieve the inversion of the object’s ground reflection characteristics of high-resolution visible images. Second, the reflectance information of the specified infrared band can be deduced combined with the reflectance matching of the typical ground object spectral library. On this basis, combined with the characterization modeling of remote-sensing imaging link—such as atmosphere, optical system, platform, and detector—a high-resolution infrared remote-sensing image reconstruction method is proposed. Based on the imaging degradation model between high- and low-resolution infrared images, the results of high-resolution reconstruction were corrected using measured infrared images. Lastly, the final fusion-super-resolution image can be obtained. The results show that the proposed fusion-super-resolution method yields high-resolution infrared images more realistically than traditional image fusion algorithms and shows better structural similarity index and visual information fidelity results, verifying the effectiveness of the proposed infrared remote-sensing image fusion-super-resolution method.

Keywords

• Infrared image super-resolution
• Remote sensing imaging
• Spectral reflectance
• Characteristic deduction
• Image reconstruction
• Error correction