Atmospheric correction algorithm based on deep learning with spatial-spectral feature constraints for broadband optical satellites: Examples from the HY-1C Coastal Zone Imager

Abstract

Broadband optical satellites have been widely used for fine monitoring of coastal waters and inland lakes for their high spatial resolution. Atmospheric correction (AC) is one of the essential data processing steps for remote sensing of water environments with high spatial resolution satellites. Most broadband optical satellites, such as HY-1C Coastal Zone Imager (CZI) with a spatial resolution of 50 m, lack of near-infrared and shortwave infrared bands needed for atmospheric correction, making accurate AC difficult. Auxiliary aerosol data is often needed for accurate AC of broadband optical satellite data, which suffers from the differences in spatial resolution and imaging time between auxiliary data and satellite data. Furthermore, existing AC algorithms always perform AC pixel by pixel, which ignores the spatial relationship between adjacent pixels. Taking HY-1C CZI as an example, this paper proposes a novel atmospheric correction algorithm based on deep learning (SSACNet). Considering the inherent spatial-spectral features of satellite images, the SSACNet combines 2D and 3D convolution. The 3D convolution was used to mine the spatial and spectral features of the image and 2D convolution was explored to perform spatial information compensation. The SSACNet was trained and evaluated using the spatio-temporally synchronized dataset of HY-1C CZI and Landsat8 Operational Land Imager (OLI). The evaluation results by in-situ data show that the SSACNet has good performance, with the average correlation coefficient of 0.89, and the absolute percentage deviation (APD) of four bands ranging from 21.53 % to 35.41 %. Compared with the quasi-synchronous Landsat8 OLI remote sensing reflectance (Rrs), the APD is less than 10 %. Compared with traditional atmospheric correction algorithms, SSACNet has significantly improved the spatial and spectral information fidelity. In addition, SSACNet also shows good applicability, as it can be used in both clear ocean waters and turbid coastal waters. This study lays a foundation for the quantitative remote sensing of water environment by broadband optical satellites.