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Open AccessArticle

Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network

by Wen Ma 1,2,3, Zongxu Pan 1,3,*, Feng Yuan 4 and Bin Lei 1,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
3
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China
4
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2578; https://doi.org/10.3390/rs11212578
Received: 25 September 2019 / Revised: 27 October 2019 / Accepted: 1 November 2019 / Published: 3 November 2019
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the resolution of remote sensing images. Different from previous super-resolution (SR) approaches based on generative adversarial networks (GANs), the novelty of our method mainly lies in the following factors. First, we made a breakthrough in terms of network architecture to improve performance. We designed a dense residual network as the generative network in GAN, which can make full use of the hierarchical features from low-resolution (LR) images. We also introduced a contiguous memory mechanism into the network to take advantage of the dense residual block. Second, we modified the loss function and altered the model of the discriminative network according to the Wasserstein GAN with a gradient penalty (WGAN-GP) for stable training. Extensive experiments were performed using the NWPU-RESISC45 dataset, and the results demonstrated that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective. View Full-Text
Keywords: single image super-resolution (SISR); remote sensing images; generative adversarial network (GAN); dense residual network (DRN); Wasserstein GAN with gradient penalty (WGAN-GP) single image super-resolution (SISR); remote sensing images; generative adversarial network (GAN); dense residual network (DRN); Wasserstein GAN with gradient penalty (WGAN-GP)
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MDPI and ACS Style

Ma, W.; Pan, Z.; Yuan, F.; Lei, B. Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network. Remote Sens. 2019, 11, 2578.

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