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All Journal ComEngApp : Computer Engineering and Applications Journal
Rika Sustika
Research Center for Informatics, LIPI
Computer Science & IT Engineering

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Evaluation of Deep Convolutional Neural Network with Residual Learning for Remote Sensing Image Super Resolution Rika Sustika
Computer Engineering and Applications Journal Vol 10 No 1 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422.617 KB) | DOI: 10.18495/comengapp.v10i1.351

Abstract

Remote sensing images generally have low spatial resolution because of the limitations of sensing devices, bandwidth transmission, or storage capacity. An effective way to improve spatial resolution with low cost is by using algorithm based approach, known as super resolution (SR). In recent years, deep learning is super resolution technique that received special attention because it gave better performance than traditional method. In this research, we evaluated two simple deep learning architectures and explored parameters setting of deep convolutional neural network with residual learning, to achieve the trade-off between performance and speed or computational complexity, for implementation on remote sensing image super resolution. Results from the experiment show that deeper network with smaller number of filter gives faster model than shallow network with bigger number of filter, without sacrificing the performance.
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