[1]BI Xiaojun,PAN Mengdi.Super-resolution reconstruction of airborne remote sensing images based on the generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2020,15(1):74-83.[doi:10.11992/tis.202002002]
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Super-resolution reconstruction of airborne remote sensing images based on the generative adversarial networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 1 Page number: 74-83 Column: 学术论文—机器感知与模式识别 Public date: 2020-01-05
Title:
Super-resolution reconstruction of airborne remote sensing images based on the generative adversarial networks
Author(s):
BI Xiaojun1;  PAN Mengdi2
1. School of Information Engineering, Minzu University of China, Beijing 100081, China;
2. Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
airborne remote sensing; super-resolution reconstruction; deep learning; residual in residual dense block; feature extraction; jump connection; Wasserstein; generative adversarial network
CLC:
TP751.1
DOI:
10.11992/tis.202002002
Abstract:
To solve the problem that the quality of airborne remote sensing images is susceptible to environmental impacts, super-resolution reconstruction is carried out. The existing super-resolution reconstruction methods for deep learning airborne remote sensing images has the problems of poor feature extraction capability, smooth edges of reconstructed images and difficulty in model training, the image reconstruction effect is enhanced to solve the above problems. The generative adversarial network is taken as the overall framework of the model. The dense residual block is used to enhance the feature extraction capability of the model, and jump connection is added to effectively extract the shallow and deep features of airborne remote sensing images. The Wasserstein-type generative adversarial network optimization model training is introduced. The method can effectively reconstruct airborne remote sensing images by 4 times, and has a gain of 2 dB or so in peak signal-to-noise ratio evaluation compared with other methods for comparison. The reconstructed airborne remote sensing images are clearer in vision, richer in details and sharper in edges. The experimental results show that the method effectively improves the model feature extraction ability, optimizes the training process, and the reconstructed airborne remote sensing image has better effect.
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