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Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline
| Content Provider | MDPI |
|---|---|
| Author | Ge, Wenyi Wang, Zhitao Wang, Guigui Tan, Shihan Zhang, Jianwei |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 76 |
| e-ISSN | 22264310 |
| DOI | 10.3390/aerospace8030076 |
| Journal | Aerospace |
| Issue Number | 3 |
| Volume Number | 8 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-15 |
| Access Restriction | Open |
| Subject Keyword | Aerospace Imaging Science Flight Simulator Remote Sensing Image Super-resolution Generative Adversarial Network |
| Content Type | Text |
| Resource Type | Article |
