Restoration of Semantic-Based Super-Resolution Aerial Images
Keywords:
aerial images, super-resolution, semantic segmentation, convolutional neural networks, visual transformers, generative adversarial networksAbstract
Currently, technologies for remote sensing image processing are actively developing, including both satellite images and aerial images obtained from video cameras of unmanned aerial vehicles. Often such images have artifacts such as low resolution, blurred image fragments, noise, etc. One way to overcome such limitations is to use modern technologies to restore super-resolution images based on deep learning methods. The specificity of aerial images is the presentation of texture and structural elements in a higher resolution than in satellite images, which objectively contributes to better results of restoration. The article provides a classification of super-resolution methods based on the main architectures of deep neural networks, namely convolutional neural networks, visual transformers and generative adversarial networks. The article proposes a method for reconstructing super-resolution aerial images SemESRGAN taking into account semantic features by using an additional deep network for semantic segmentation during the training stage. The total loss function, including adversarial losses, pixel-level losses, and perception losses (feature similarity), is minimized. Six annotated aerial and satellite image datasets CLCD, DOTA, LEVIR-CD, UAVid, AAD, and AID were used for the experiments. The results of image restoration using the proposed SemESRGAN method were compared with the basic architectures of convolutional neural networks, visual transformers and generative adversarial networks. Comparative results of image restoration were obtained using objective metrics PSNR and SSIM, which made it possible to evaluate the quality of restoration using various deep network models.
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23. Haykir A.A., Öksuz I. Super-resolution with generative adversarial networks for improved object detection in aerial images. Information Discovery and Delivery. 2023. vol. 51. no. 4. pp. 349–357.
24. Tuna C., Unal G., Sertel E. Single-frame super resolution of remote-sensing images by convolutional neural networks. Int. J. Remote Sens. 2018. vol. 39. no. 8. pp. 2463–2479.
25. Dong C., Loy C.C., He K., Tang, X. Learning a deep convolutional network for image super-resolution // Computer Vision – ECCV 2014: 13th European Conference. 2014. pp. 184–199.
26. Wang J., Wang B., Wang X., Zhao Y., Long T. Hybrid attention-based U-shaped network for remote sensing image super-resolution. IEEE Transactions on Geoscience and Remote Sensing. 2023. vol. 61. pp. 1–15.
27. Gu J., Dong C. Interpreting super-resolution networks with local attribution maps. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021. pp. 9199–9208.
28. Wang X., Yu K., Wu S., Gu J., Liu Y., Dong C., Qiao Y., Loy C.C. ESRGAN: Enhanced super-resolution generative adversarial networks. Computer Vision – ECCV 2018 Workshops. 2019. pp. 63–79.
29. Johnson J., Alahi A., Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution. Computer Vision – ECCV 2016: 14th European Conference. 2016. pp. 694–711.
30. Liu M., Chai Z., Deng H., Liu R. A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022. vol. 15. pp. 4297–4306.
31. Xia G., Bai X., Ding J., Zhu Z., Belongie S., Luo J., Datcu M., Pelillo M., Zhang L. DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. pp. 3974–3983.
32. Chen H., Shi Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 2020. vol. 12. no. 10. DOI: 10.3390/rs12101662.
33. Lyu Y., Vosselman G., Xia G-S., Yilmaz A., Yang M.Y. UAVid: A semantic segmentation dataset for UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2020. vol. 165. pp. 108–119.
34. Airbus Aircraft Detection. Available at: www.kaggle.com/datasets/airbusgeo/airbus-aircrafts-sample-dataset (accessed 04.03.2024).
35. Xia G.-S., Hu J., Hu F., Shi B., Bai X., Zhong Y., Zhang L. AID: A benchmark dataset for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing. 2017. vol. 55. no. 7. pp. 3965–3981.
36. Zhang R., Isola P., Efros A.A., Shechtman E., Wang O. The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE: Salt Lake City, UT, USA. 2018. pp. 586–595.
Published
2024-06-26
How to Cite
Favorskaya, M., & Pakhirka, A. (2024). Restoration of Semantic-Based Super-Resolution Aerial Images. Informatics and Automation, 23(4), 1047-1076. https://doi.org/10.15622/ia.23.4.5
Section
Artificial Intelligence, Knowledge and Data Engineering
Copyright (c) Маргарита Николаевна Фаворская, Андрей Иванович Пахирка
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