PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening

@article{Xing2022PCGANsPC,
  title={PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening},
  author={Yinghui Xing and Shuyuan Yang and Songhong Wang and Yan Zhang and Yanning Zhang},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.14451}
}
The fusion of multispectral and panchromatic images is always dubbed pansharpening. Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network. However, remote sensing images always have large variations, as a result, these one-step methods are vulnerable to the error accumulation and thus incapable of preserving spatial details as well as the spectral… 
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References

SHOWING 1-10 OF 40 REFERENCES

Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening

Experiments on images acquired by Quickbird and GaoFen-1 satellites demonstrate that the proposed PSGAN can fuse PAN and MS images effectively and significantly improve the results over the state of the art traditional and CNN based pan-sharpening methods.

Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion

Generative Adversarial Network for Pansharpening With Spectral and Spatial Discriminators

A new method based on a bidiscriminator in a generative adversarial network (GAN) framework, and called MDSSC-GAN SAM, considers a spatial and a spectral constraint in the loss function of the generator.

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.

GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening

Least Squares Generative Adversarial Networks

This paper proposes the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator, and shows that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence.

A New Pan-Sharpening Method With Deep Neural Networks

Comparative experimental results with several quality assessment indexes show that the proposed method outperforms other pan-sharpening methods in terms of visual perception and numerical measures.

Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network

Through both quantitative and visual assessments on a large number of high-quality MS images from various sources, it is confirmed that the proposed model is superior to all the mainstream algorithms included in the comparison, and achieves the highest spatial–spectral unified accuracy.

Image Super-Resolution Using Deep Convolutional Networks

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep…

A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

This work proposes the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery and yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.