High-quality super-resolution mapping using spatial deep learning.

ABSTRACT: 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 i

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10241974 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature