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Open AccessArticle

Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

1
Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
2
School of Computer Science, Wuhan University, Wuhan 430072, China
3
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1588; https://doi.org/10.3390/rs11131588
Received: 13 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 4 July 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities. View Full-Text
Keywords: satellite imagery; super-resolution; residual network; multi-scale image; convolutional neural network satellite imagery; super-resolution; residual network; multi-scale image; convolutional neural network
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MDPI and ACS Style

Lu, T.; Wang, J.; Zhang, Y.; Wang, Z.; Jiang, J. Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sens. 2019, 11, 1588. https://doi.org/10.3390/rs11131588

AMA Style

Lu T, Wang J, Zhang Y, Wang Z, Jiang J. Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sensing. 2019; 11(13):1588. https://doi.org/10.3390/rs11131588

Chicago/Turabian Style

Lu, Tao; Wang, Jiaming; Zhang, Yanduo; Wang, Zhongyuan; Jiang, Junjun. 2019. "Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network" Remote Sens. 11, no. 13: 1588. https://doi.org/10.3390/rs11131588

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