Evaluation of Deep Convolutional Neural Network with Residual Learning for Remote Sensing Image Super Resolution
| Autor: | Rika Sustika |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Deep learning 0211 other engineering and technologies 02 engineering and technology Filter (signal processing) Residual Convolutional neural network Transmission (telecommunications) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Geriatrics and Gerontology business Image resolution 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | Computer Engineering and Applications Journal. 10:1-8 |
| ISSN: | 2252-5459 2252-4274 |
| DOI: | 10.18495/comengapp.v10i1.351 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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