Cross-sensor remote sensing imagery super-resolution via an edge-guided attention-based network

The deep learning based super-resolution (SR) methods have recently achieved remarkable progress in the reconstruction of ideally simulated high-quality remote sensing image datasets. However, due to the large variation in image quality caused by the complex degradation factors, their performance decreases dramatically on real-world images acquired by different satellite sensors. To this end, we propose a cross-sensor SR framework that consists of a cross-sensor degradation modeling strategy for bridging the gap between the images obtained by the source and target sensors, and an edge-guided attention-based SR (EGASR) network to promote the learning of high-frequency feature representation. Specifically, we build a degradation pool on the low-resolution (LR) target sensor to produce a degraded training dataset simulated from the high-resolution (HR) images obtained by the source sensor. Furthermore, the EGASR network, which employs the edge-guided residual attention block (EGRAB) to introduce implicit edge prior to enhance edge-related information, is embedded in the cross-sensor SR framework for reconstructing HR results with sharp details. The proposed method is applied on images from the Chinese Gaofen (GF) satellite sensors and compared to several representative SR methods. An ideally simulated GF-2 LR/HR image set with only downsampling considered is first used to evaluate the effectiveness of the proposed EGASR network. Moreover, GF-2/GF-1 and GF-2/GF-6 cross-sensor SR datasets are constructed by synthesizing GF-2 degraded image pairs with the degradation pools estimated from the GF-1 and GF-6 images, respectively. The results show that: 1) the proposed EGASR model shows superiority in reconstructing textural details and edge features, and achieves the best results among the state-of-art SR methods involved in comparison; 2) the cross-sensor SR framework significantly promotes the model’s ability to super-resolve real-world LR images acquired by the target satellite sensors, e.g., the NIQE values are improved by at least 30% and 34% on average with respect to other comparative methods for GF-2/GF-1 and GF-2/GF-6 datasets in the real experiments, respectively. Code is available at https://github.com/zhonghangqiu/EGASR.