A remote sensing satellite image compression method based on conditional generative adversarial network

Kan Cheng; Yafang Zou; Yuting Zhao; Hao Jin; Chengchao Li

doi:10.1117/12.2683879

19 October 2023 A remote sensing satellite image compression method based on conditional generative adversarial network

Kan Cheng, Yafang Zou, Yuting Zhao, Hao Jin, Chengchao Li

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 1273314 (2023) https://doi.org/10.1117/12.2683879
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Conference Poster

Abstract

With the sharp increase of images on satellites, the efficiency of satellite-to-ground data transmission has become a bottleneck that restricts the effectiveness of remote sensing satellites. To alleviate the pressure of data transmission, we conducted in-depth research on remote sensing satellite image compression technology. Traditional methods and existing deep learning methods are prone to losing detailed information when dealing with remote sensing satellite images with complex textures and rich details. Given that Generative Adversarial Networks (GAN) have advantages in texture generation and detail restoration, we propose a remote sensing satellite image compression method based on conditional GAN. Our main innovations are: 1) proposing a compression framework for remote sensing satellite images based on conditional GAN, which improves the reconstruction quality through adversarial learning between the conditional generator and discriminator. 2) introducing the Laplacian of Gaussian loss to train the model, which emphasizes details such as edges, contours, and textures in remote sensing images. 3) introducing multiple perceptual metrics to calculate the similarity between images, which comprehensively evaluates the quality of reconstructed images. Experimental results show that our method has better visual effects and objective evaluation indicators than traditional methods and existing deep learning methods at the same compression ratio.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kan Cheng, Yafang Zou, Yuting Zhao, Hao Jin, and Chengchao Li "A remote sensing satellite image compression method based on conditional generative adversarial network", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 1273314 (19 October 2023); https://doi.org/10.1117/12.2683879

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