Location Aware Super-Resolution for Satellite Data Fusion

Olaoluwa Adigun, Peder A. Olsen, Ranveer Chandra

Abstract: 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 (Pléiades) from a public satellite source (Sentinel-2). Each super-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.