Location Aware Super-Resolution for Satellite Data Fusion

Satellite data fusion involves images with different spatial, temporal, and spectral resolution. These images are taken under different illumination conditions, with different sensors and atmospheric noise. We use classic super-resolution algorithms to synthesize commercial satellite images from a public satellite source (Sentinel-2). Each super-resolution resolution method is then further improved by adaptive sharpening to the location by use of matrix completion (regression with missing pixels). Finally, we consider ensemble systems and a residual channel attention dual network with stochastic dropout. The resulting systems are visibly less blurry with higher fidelity and yield improved performance

Example of several super-resolution methods from 6 locations. The standard super-resolution methods are notably blurrier compared to the ground truth in column 1. The ensembles combine the 3 preceding columns using SRCNN.