A Pixel-Based Machine Learning Atmospheric Correction for PeruSAT-1 Imagery

Atmospheric correction is essential in remote sensing, as it reduces the effects of light absorption and scattering by suspended particles and gases, enabling accurate surface reflectance computation from the observed Top-of-Atmosphere (TOA) reflectance. Each satellite sensor requires a customized atmospheric correction processor due to its unique system characteristics. Currently, PeruSAT-1, the first Peruvian remote sensing satellite, does not include this capability in its image processing pipeline, which poses challenges for its effectiveness in defense, security, and natural disaster management applications. This research investigated pixel-based machine learning methods for atmospheric correction of PeruSAT-1, using Sentinel-2 harmonized Bottom-of-Atmosphere (BOA) surface reflectance images as a benchmark, alongside additional atmospheric, terrain, and acquisition parameters. A robust dataset was developed to align data across temporal, spatial, geometric, and contextual conditions. Experimental results showed R2 values between 0.886 and 0.938, and RMSE values ranging from 0.009 to 0.025 compared to the benchmarks. Among the models tested, the Feedforward Neural Network (FFNN) using the Leaky ReLU activation function achieved the best overall performance. These findings confirm the robustness of this approach, offering a scalable methodology for satellites with similar characteristics and establishing a foundation for a customized atmospheric correction pipeline for PeruSAT-1. Future work will focus on diversifying the dataset across spectral and seasonal conditions and optimizing the modeling to address challenges in shorter wavelengths and high-reflectance surfaces.