A CBAM‐GAN‐based method for super‐resolution reconstruction of remote sensing image

Autor: Longbao Wang, Qing Yu, Xin Li, Hui Zeng, Hailong Zhang, Hongmin Gao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Image Processing, Vol 18, Iss 2, Pp 548-560 (2024)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.12968
Popis: Abstract As satellite imagery technology advances, remote sensing plays an increasingly prominent role in modern society. Nevertheless, the limitations of existing imaging sensors and complex atmospheric conditions constrain the quality of raw remote sensing data, posing challenges for interpretation and noise reduction. Super‐resolution technology focuses on enhancing low‐quality, low‐resolution remote sensing images. In this study, we introduce a method that utilizes a high‐order degradation model to generate low‐resolution remote sensing images. We employ a Generative Adversarial Network with a Convolutional Block Attention Module (CBAM‐GAN) to enhance these images, reducing noise interference and improving texture and feature display. Our approach outperforms other methods on the UCMerced‐LandUse, WHU‐RS19, and AID datasets. Specifically, it raises SSIM index scores to 0.9443, 0.8928, and 0.8633, respectively, exceeding baselines by 1.31%, 0.19%, and 1.30%. The MOS index also improves to 3.98, 3.96, and 3.83, respectively, representing a 2.31%, 8.20%, and 2.96% gain over the baseline. Our reconstruction produces superior results, demonstrating the effectiveness of our proposed method.
Databáze: Directory of Open Access Journals
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