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

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

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 05 Page number: 8-16 Column: Public date: 2022-08-22

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

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

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

CLC:: P237TP391

DOI:: 10.13705/j.issn.1671-6833.2022.05.013

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.

References:: [1] YANG J C, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE transactions on image processing, 2010, 19(11): 2861-2873.
[2] PERONA P, BRANSON S J, WEGNER J D, et al. System and method for locating and performing fine grained classification from multi-view image data: US10534960[P]. 2020-01-14.

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Last Update: 2022-08-20