Super-resolution reconstruction of remote sensing images based on SRGAN

Mengbei Yu; Hongjuan Wang; Chang Liu; Deping Lin

doi:10.1117/12.2653847

18 November 2022 Super-resolution reconstruction of remote sensing images based on SRGAN

Mengbei Yu, Hongjuan Wang, Chang Liu, Deping Lin

Proceedings Volume 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022); 124730U (2022) https://doi.org/10.1117/12.2653847
Event: Second International Conference on Optics and Communication Technology (ICOCT 2022), 2022, Hefei, China

Abstract

In the field of remote sensing images, due to the limitations of hardware equipment, image transmission, natural environment and other reasons, the resolution of the obtained remote sensing images cannot reach the desired resolution. The emergence of image super-resolution reconstruction technology can improve the resolution of remote sensing images without increasing the high cost. Image super-resolution reconstruction refers to the fact that low-resolution images can obtain high-resolution images through certain algorithmic techniques. With the rapid development of deep learning ideas, researchers have applied it to the field of image super-resolution reconstruction and achieved good results. Image super-resolution reconstruction also shifts from traditional reconstruction methods to deep learning-based methods. The emergence of the idea of Generative Adversarial Networks has further advanced the field of image super-resolution reconstruction. By using the idea of Generative Adversarial Network (GAN), researchers can obtain more realistic high-resolution images. This paper mainly uses the SRGAN model, the image dataset DIV2K for super-resolution reconstruction, and uses a dense residual structure in the generator network to obtain more image information, so that the effect of image reconstruction is more realistic. Through the experimental verification on the SIRI-WHU remote sensing test data set, the two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are compared, and the effect is improved. Better generation results can also be observed through subjective human vision.

Citation Download Citation

Mengbei Yu, Hongjuan Wang, Chang Liu, and Deping Lin "Super-resolution reconstruction of remote sensing images based on SRGAN", Proc. SPIE 12473, Second International Conference on Optics and Communication Technology (ICOCT 2022), 124730U (18 November 2022); https://doi.org/10.1117/12.2653847

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KEYWORDS

Super resolution

Remote sensing

Convolution

Reconstruction algorithms

Signal to noise ratio

Visualization

Image resolution

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