High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN

To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) in this paper. Firstly, based on the global radiation scattering mechanism, the HFIRSIGM_GRSMP model is constructed to address the problem of accurately characterizing factors that affect fidelity—such as the random distribution of the radiation field, multipath scattering, and nonlinear changes—through the innovative fusion of physical models and deep learning. This model accurately characterizes the complex radiation field distribution and the image detail-feature mapping relationship from visible-to-infrared remote sensing. Then, 8000 pairs of image datasets were constructed based on Landsat 8 and Sentinel-2 satellite data. Finally, the experiment demonstrates that the average SSIM of images generated using HFIRSIGM_GRSMP reaches 89.16%, and all evaluation metrics show significant improvement compared to the contrast models. More importantly, this method demonstrates high accuracy and strong adaptability in generating short-wave, mid-wave, and long-wave infrared remote sensing images. This method provides a more comprehensive solution for generating high-fidelity infrared remote sensing images.