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Superresolution Approach of Remote Sensing Images based on Deep Convolutional Neural Network

Volume 14, Number 3, March 2018, pp. 463-472
DOI: 10.23940/ijpe.18.03.p7.463472

Jitao Zhanga, Aili Wanga, Na Anb, and Yuji Iwahoric

aHigher Education Key Lab for Measure& Control Technology and Instrumentations of Heilongjiang, Harbin University of Science and Technology, Harbin, 150080, China
bHytera Communications Corporation Limited, Harbin, 150001, China
cDepartment of Computer Science, Chubu University, Aichi, Japan

(Submitted on December 6, 2017; Revised on January 16, 2018; Accepted on February 12, 2018)


Abstract:

Nowadays, remote sensing images have been widely used in civil and military fields. But, because of the limitations of the current imaging sensors and complex atmospheric conditions, the resolution of remote sensing images is often low. In this paper, a superresolution reconstruction algorithm based on the deep convolution neural network to improve the resolution of the remote sensing image is proposed. First, this algorithm learned a series of features of the mapping between high and low resolution images in the training phase. This mapping is expressed as a kind of deep convolutional neural network; the trained network is a series of parameter optimization for super-resolution reconstruction of remote sensing image. Experimental results show that the superresolution algorithm proposed in this paper can keep the details subjectively and improve the evaluation index objectively.

 

References: 18

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