GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution

Abstract

Superresolution (SR) of remote sensing images aims to restore high-quality information from low-resolution images. Recently, it has witnessed great strides with the rapid development of deep learning (DL) techniques. Despite their good performance, these DL-based models are often ineffective in balancing global and local feature extraction. Moreover, they are usually hindered by the poor image reconstruction capability of the decoder inside their SR models. To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture. In particular, the proposed encoder is endowed with nonlocal sparse attention modules incorporated into the residual dense network to learn robust representations from global features. Furthermore, a decoder equipped with back-sampling blocks is devised to fully exploit the feature maps extracted from the encoder. Extensive experimental comparisons based on two multisensor satellite remote sensing datasets confirm that the proposed GCRDN achieves impressive performance in terms of perceptual quality and fidelity.

Keywords

• Convolutional neural network (CNN)
• nonlocal sparse attention
• remote sensing images
• superresolution (SR)