EGDSR: Encoder-Generator-Decoder Network for Remote Sensing Super-Resolution Reconstruction
Baikai Sui, Yungang Cao, Shuang Zhang · IEEE Geoscience and Remote Sensing Letters · 2023
Remote sensing single-image super-resolution reconstruction process detail information is easy to be lost and prone to defocus phenomenon, in order to solve these problems, we are based on the idea of potential spatial vector mapping, focusing on the attribute control of the feature recovery process, to improve the quality of remote sensing image reconstruction. This letter proposes an encoder-generator-decoder super-resolution reconstruction network for remote sensing named EGDSR. We design three modules: multiscale feature extraction and latent code generation module, multi-attribute control of resolution progression module (recovery of latent encoding, fusion of multi-scale features, and generation of high-resolution features), high-resolution image reconstruction module. The experimental results show that the high-resolution images generated by our proposed EGDSR network have a stronger sense of truth, richer texture, and more realistic details, and have a better perceptual effect compared with other state-of-the-art super-resolution networks. In addition, we also combine the semantic segmentation task to assist in verifying the quality of the high-resolution remote sensing images generated by EGDSR, and successfully verify the higher application value of our proposed method.