CGC-Net: A Context-Guided Constrained Network for Remote-Sensing Image Super Resolution

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 12; p. 3171
• Main Authors: Zheng, Pengcheng, Jiang, Jianan, Zhang, Yan, Zeng, Chengxiao, Qin, Chuanchuan, Li, Zhenghao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Algorithms; Consistency; Constraints; Context; Deep learning; Dictionaries; distance transform; Embedding; guided filter; Hydrofluorocarbons; Image processing; Image quality; Image reconstruction; Image resolution; Image segmentation; Machine learning; Methods; Neural networks; Remote sensing; remote-sensing image; Satellites; Semantics; super-resolution
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15123171

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.