High-quality super-resolution mapping using spatial deep learning

Super-resolution mapping (SRM) is a critical technology in remote sensing. Recently, several deep learning models have been developed for SRM. Most of these models, however, only use a single stream to process remote sensing images and mainly focus on capturing spectral features. This can undermine the quality of the resulting maps. To address this issue, we propose a soft information-constrained network (SCNet) for SRM that leverages spatial transition features represented by soft information as a spatial prior. Our network incorporates a separate branch to process prior spatial features for feature enhancement. SCNet can extract multi-level feature representations simultaneously from both remote sensing images and prior soft information and hierarchically incorporate features from soft information into image features. Experimental results on three datasets demonstrate that SCNet generates more complete spatial details in complex areas, providing an effective means for producing high-quality and high-resolution mapping products from remote sensing images.