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Sensors 2018, 18(4), 1194; https://doi.org/10.3390/s18041194

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
2
National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 13 March 2018 / Revised: 10 April 2018 / Accepted: 11 April 2018 / Published: 13 April 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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Abstract

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods. View Full-Text
Keywords: super-resolution; video satellite; deep convolutional network super-resolution; video satellite; deep convolutional network
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Xiao, A.; Wang, Z.; Wang, L.; Ren, Y. Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network. Sensors 2018, 18, 1194.

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