Local-Global Context-Aware Generative Dual-Region Adversarial Networks for Remote Sensing Scene Image Super-Resolution

Recently, high-resolution (HR) remote sensing images (RSIs) have attracted increasing attention in a number of tasks. Super-resolution (SR) is an efficient method to obtain HR RSIs. Due to the influence of imaging distances and angles, RSIs significantly differ from natural images in terms of land cover element distribution, ground object scale, and scene complexity. This poses a challenge for capturing global and local low and high-frequency and restoring fine image details for RSI SR. In this article, a local-global context-aware generative dual-region adversarial network (LGC-GDAN) is designed for RSI SR. It is composed of dual region-level discriminators and a dual-path generator with a context-aware network and an edge-assisted network. To capture global and local low and high-frequency information, the global-aware self-attention (GAS) mechanism and local-aware self-attention (LAS) mechanism are introduced into the context-aware network. The GAS mechanism combines high-pass and low-pass filtering for long-range similarity feature, while LAS uses local aggregation for fine-level feature. The low-resolution (LR) images and the corresponding edge maps are input to the edge-assisted network to extract the detailed geometric structure. To address small ground object and complex ground scenes, conventional image-level discriminators exhibit limited performance in capturing detailed information. Unlike previous discriminator, a region-level discriminator is designed to obtain the real/fake label of each local region. Moreover, two task-driven loss functions are designed to produce diverse images for further scene classification. The experiments undertaken on several remote sensing datasets demonstrate that LGC-GDAN outperforms the other state-of-the-art methods.