Impact of different compression rates for hyperspectral data compression based on a convolutional autoencoder

Kuester, Jannick; Gross, Wolfgang L.; Heizmann, Michael; Middelmann, Wolfgang

doi:10.1117/12.2598010

Cited by 1 publication

(1 citation statement)

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“…Currently, the on-orbit remote sensing satellite image compression methods mainly consist of traditional image compression methods such as JPEG2000, JPEG-LS, BPG, etc. 1 These methods exhibit good reconstruction quality within a certain range of compression ratios. However, traditional image compression methods suffer from inherent flaws such as blockiness, blurring, and ringing artifacts, 2 making it difficult to balance high compression ratios and high reconstruction quality.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng,

Zou,

Zhao

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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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.

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Section: Introductionmentioning

confidence: 99%