[1]成科扬,荣兰,蒋森林,等.基于深度学习的遥感图像超分辨率重建技术综述[J].郑州大学学报(工学版),2022,43(05):8-16.[doi:10.13705/j.issn.1671-6833.2022.05.013]
　CHENG Keyang,RONG Lan,JIANG Senlin,et al.Overview of Methods for Remote Sensing Image Super-resolution Reconstruction Based on Deep Learning[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):8-16.[doi:10.13705/j.issn.1671-6833.2022.05.013]

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基于深度学习的遥感图像超分辨率重建技术综述()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

43

期数:

2022年05期

页码:

8-16

栏目:

出版日期:

2022-08-22

文章信息/Info

Title:

Overview of Methods for Remote Sensing Image Super-resolution Reconstruction Based on Deep Learning

作者:

成科扬; 荣兰 102;  102); background-color: rgb(255;  255;  255); font-family: Arial;  Verdana;  sans-serif; font-size: 12pt;">; 蒋森林; 詹永照

江苏大学计算机科学与通信工程学院;镇江昭远智能科技有限公司;江苏省大数据泛在感知与智能农业应用工程研究中心;

Author(s):

CHENG Keyang¹; 2; 3;  RONG Lan¹;  JIANG Senlin¹;  ZHAN Yongzhao¹; 2; 3

1.School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013,China；

2.Zhenjiang Zhaoyuan Intelligent Technology Co., Ltd., Zhenjiang 212013, China；

3.Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center， Zhenjiang 212013, China

关键词:

遥感图像; 超分辨率重建; 深度学习; 卷积神经网络; 生成对抗网络

Keywords:

remote sensing image;  super-resolution reconstruction;  deep learning;  convolutional neural networks;  generative adversarial network

分类号:

P237TP391

DOI:

10.13705/j.issn.1671-6833.2022.05.013

文献标志码:

A

摘要:

遥感图像超分辨率重建技术是计算机视觉中的重要技术。近年来，得益于深度学习的成功，基于深度学习的遥感图像超分辨率技术正在蓬勃发展，在许多领域被广泛应用。本文首先回顾传统遥感图像超分重建算法并引出基于深度学习的遥感图像超分重建方法；然后总结了单幅遥感图像、多幅遥感图像和多/高光谱遥感图像超分重建方法中具有代表性的基于深度学习的方法，我们从模型类型、网络结构和重建效果等方面对各种方法进行分析和评价，并对比了它们的优缺点；最后对遥感图像超分重建技术存在的问题进行分析和未来的发展做出展望。

Abstract:

Remote sensing image super-resolution reconstruction based on deep learning is one of the most important methods in computer vision. The traditional super-resolution reconstruction method of remote sensing image could not meet the needs of ground object recognition, detailed land detection and other applications, This study aimed to solve the problem by using deep learning to reconstruct the resolution of remote sensing image. After reviewing the latest research status at home and abroad, this paper divides deep learn-based remote sensing image super-resolution reconstruction methods were classified into three categories, includeing single remote sensing image, multi-remote sensing image and multi-hyperspectral remote sensing image super-resolution reconstruction methods. The methods of super-resolution reconstruction of single remote sensing image based on deep learning were systematically examined, including multi-scale feature extraction method, combined with wavelet transform method, hourglass generation network method, edge enhancement network method and cross-sensor method. The current mainstream methods of multi-remote sensing image and multi-hyperspectral remote sensing image super-resolution reconstruction were also examined based on deep learning. Through the analysis of the experimental results, the best single image reconstruction method is based on GAN, but the effect of multi-remote sensing image and multi-hyperspectral remote sensing image reconstruction was still not good enough, there were several prablems, such as registration fusion, multi-source information fusion and other soon. Finally, the future development trend of remote sensing image super-resolution reconstruction method based on deep learning was explored, The future research trend could be building neural network structure according to the characteristics of remote sensing image, unsupervised learning remote sensing image super-resolution reconstruction method, and multi-source remote sensing image super-resolution reconstruction method.

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