Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images

doi:10.3390/app13031245

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images
	Z. P. Ren, J. P. Zhao, C. Y. Chen, Y. Lou and X. C. Ma
刊名	Applied Sciences-Basel
	2023
卷号	13 期号:3 页码:15
DOI	10.3390/app13031245
英文摘要	Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67817]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. P. Ren, J. P. Zhao, C. Y. Chen, Y. Lou and X. C. Ma. Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images[J]. Applied Sciences-Basel,2023,13(3):15.
APA	Z. P. Ren, J. P. Zhao, C. Y. Chen, Y. Lou and X. C. Ma.(2023).Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images.Applied Sciences-Basel,13(3),15.
MLA	Z. P. Ren, J. P. Zhao, C. Y. Chen, Y. Lou and X. C. Ma."Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images".Applied Sciences-Basel 13.3(2023):15.