Name
Abstract | ||
|---|---|---|
Due to the inevitable existence of clouds and their shadows in optical remote sensing images, certain ground-cover information is degraded or even appears to be missing, which limits analysis and utilization. Thus, cloud removal is of great importance to facilitate downstream applications. Motivated by the sparse representation techniques which have obtained a stunning performance in a variety of ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2018.2829028 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Clouds,Remote sensing,Image reconstruction,Sparse matrices,Image sequences,Robustness,Satellites | Iterative reconstruction,Computer vision,Anomaly detection,Sparse approximation,Robust principal component analysis,Robustness (computer science),Pixel,Artificial intelligence,Real image,Mathematics,Cloud computing | Journal |
Volume | Issue | ISSN |
15 | 7 | 1545-598X |
Citations | PageRank | References |
2 | 0.45 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wen | 1 | 27 | 5.25 |
| Yongjun Zhang | 2 | 164 | 33.87 |
| Zhi Gao | 3 | 33 | 10.15 |
| xiaoling chen | 4 | 155 | 27.17 |