Feature Intensification Using Perception-Guided Regional Classification for Remote Sensing Image Super-Resolution

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-11, Vol.16 (22), p.4201
• Main Authors: Li, Yinghua, Xie, Jingyi, Chi, Kaichen, Zhang, Ying, Dong, Yunyun
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; compressed sensing; Deep learning; Frequency dependence; Image reconstruction; Image resolution; Perception; Processing speed; Remote sensing; remote sensing images; Satellites; super-resolution; Texture
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16224201

In recent years, super-resolution technology has gained widespread attention in the field of remote sensing. Despite advancements, current methods often employ uniform reconstruction techniques across entire remote sensing images, neglecting the inherent variability in spatial frequency distributions, particularly the distinction between high-frequency texture regions and smoother areas, leading to computational inefficiency, which introduces redundant computations and fails to optimize the reconstruction process for regions of higher complexity. To address these issues, we propose the Perception-guided Classification Feature Intensification (PCFI) network. PCFI integrates two key components: a compressed sensing classifier that optimizes speed and performance, and a deep texture interaction fusion module that enhances content interaction and detail extraction. This network mitigates the tendency of Transformers to favor global information over local details, achieving improved image information integration through residual connections across windows. Furthermore, a classifier is employed to segment sub-image blocks prior to super-resolution, enabling efficient large-scale processing. The experimental results on the AID dataset indicate that PCFI achieves state-of-the-art performance, with a PSNR of 30.87 dB and an SSIM of 0.8131, while also delivering a 4.33% improvement in processing speed compared to the second-best method.