A Relativistic Average Generative Adversarial Network for Pan-Sharpening

被引：0
作者：
Chen T. [1 ]
Wang S. [1 ]
Gao T. [1 ]
Liu M. [1 ]
Chen Y. [1 ]
机构：
[1] School of Information Engineering, Chang'an University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2022年 / 56卷 / 03期
关键词：
Deep learning; Image fusion; Pan-sharpening; Relativistic average generative adversarial network; Spatial attention mechanism;
D O I：
10.7652/xjtuxb202203006
中图分类号：
学科分类号：
摘要：
A relativistic average generative adversarial network for pan-sharpening (Pan-RaGAN) based on a deep learning algorithm is proposed to solve the problems that details of fusion result are easy to be lost due to insufficient feature extraction from original image in the pan-sharpening process and information redundancy is caused by ignoring spatial feature difference of different regions in image fusion process. Firstly, the improved dense block structure is utilized to extract the features of the original image in the generator, so as to make full use of the features from different layers of the original image and obtain better fusion results with more details. Secondly, a feature refinement module based on spatial attention mechanism is proposed for feature selection, which can make a better trade-off between retaining effective high-frequency information and eliminating redundant information. Furthermore, the image reconstruction module is utilized to fuse the refined features with the up-sampled low resolution multispectral images to preserve the spectral information. Finally, the relativistic average discriminator is utilized to improve the loss function of the network, and further optimize the fusion effect. Experimental results on Gao Fen-2 dataset and Quick Bird dataset and a comparison with the existing generative adversarial network for remote sensing image pan-sharpening show that the spectral angle mapper index of the proposed Pan-RaGAN network is reduced by 0.075 on average, which verifies the effectiveness of the proposed Pan-RaGAN network. © 2022, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：54 / 64
页数：10
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