Super-resolution reconstruction of sea surface pollutant diffusion images based on deep learning models: a case study of thermal discharge from a coastal power plant

Abstract

While remote sensing images could convey essential information of surface water environment, the low spatial resolution limits their application. This study carried out a series of experiment tests of thermal discharge from a coastal power plant and constructed an image dataset HY_IRS, representing the transport and diffusion of discharged heated water in tidal waters. Two image super-resolution (SR) reconstruction models based on deep learning (DL), ESPCN and ESRGAN, were trained based on this dataset and then used to reconstruct high-resolution remote sensing images. It shows that the two DL models can markedly improve the spatial resolution of the surface diffusion image of thermal discharging, with the PSNR improved by 8.3% on average. The trained two models were successfully used to improve the spatial resolution of thermal infrared remote sensing SST images from Landsat8 TIRS, indicating that the SR model based on DL has a good effect and a crucial application prospect in the field of improving the resolution of pollutant diffusion remote sensing images.