CTGAN: A super-resolution end-to-end model for optical remote sensing images

Yuanwei Chen; Rui Qin; Runze Li; Yang Li; Jilin Li

doi:10.1117/12.3059067

2 January 2025 CTGAN: A super-resolution end-to-end model for optical remote sensing images

Yuanwei Chen, Rui Qin, Runze Li, Yang Li, Jilin Li

Author Affiliations +

Proceedings Volume 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024); 135140A (2025) https://doi.org/10.1117/12.3059067
Event: 2024 International Conference on Remote Sensing and Digital Earth, 2024, Chengdu, China

Abstract

As the resolution of Earth observation imagery advances, many of the downstream industries that rely on remotely sensed imagery can also advance. Due to the physical limitations of the optical sensors carried by satellites, the growth of the resolution of remote sensing images becomes difficult. Therefore, it is becoming increasingly important to boost the resolution of Earth observation images by methods other than upgrading the physical components, such as super-resolution. As a computer vision task, convolutional neural networks (CNNs) perform well on super-resolution tasks. Transformer-based models also show good performance on it. The new model proposed in this paper, Convolutional Transformer Generative Adversarial Network (CTGAN). It promotes image super-resolution by balancing local features with global features. Results on real satellite datasets demonstrate the effectiveness of the CTGAN model.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanwei Chen, Rui Qin, Runze Li, Yang Li, and Jilin Li "CTGAN: A super-resolution end-to-end model for optical remote sensing images", Proc. SPIE 13514, International Conference on Remote Sensing and Digital Earth (RSDE 2024), 135140A (2 January 2025); https://doi.org/10.1117/12.3059067

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