Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline

High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this...

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Bibliographic Details
Published inAerospace Vol. 8; no. 3; p. 76
Main Authors Ge, Wenyi, Wang, Zhitao, Wang, Guigui, Tan, Shihan, Zhang, Jianwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2021
Subjects
Aircraft
Airports
Airspace
Datasets
Design
Dictionaries
Flight simulation
flight simulator
Flight simulators
Flight training
generative adversarial network
High resolution
Image reconstruction
Image resolution
Neural networks
Noise
Remote sensing
remote sensing image
super-resolution
Visual flight
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Summary:High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this issue with another perspective based on image super-resolution (SR) technology. First, we present a new ultra-high-resolution remote sensing image dataset named Airport80, which is captured from the airspace near various airports. Second, a deep learning baseline is proposed by applying the generative and adversarial mechanism, which is able to reconstruct a high-resolution image during a single image super-resolution. Experimental results for our benchmark demonstrate the effectiveness of the proposed network and show it has reached satisfactory performances.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace8030076