Name
Abstract | ||
|---|---|---|
With the development of remote sensing techniques, remote sensing data can be obtained with higher spatial, higher spectral, and higher temporal resolution. In addition, to get higher spatial resolution, super-resolution for increasing spatial resolution is getting special attention. In this paper, we will focus on some classical learning-based superresolution methods to investigate the adaptability for satellite videos with low imaging quality. Methods include sparse representation, collaborative representation, and deep learning methods. Experiments show that learning-based methods can perform well for single-frame super-resolution for satellite videos. Methods based on deep learning show higher PSNR and SSIM. And multi-frame super-resolution will be good for moving objects. However, it may also bring negative influence for a stationary scene, which is caused by low satellite video quality, such as winkling noise, a vibration of a camera, overexposure of metals. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WHISPERS.2019.8920882 | 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
Super-Resolution,Satellite Videos,Dictionary Learning,Deep Learning. | Adaptability,Computer vision,Satellite,Exposure,Computer science,Sparse approximation,Artificial intelligence,Deep learning,Image resolution,Temporal resolution,Video quality | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-7281-5295-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huan Liu | 1 | 18 | 2.27 |
| Yanfeng Gu | 2 | 742 | 55.56 |