Name
Abstract | ||
|---|---|---|
Different empirical models have been developed for cloud detection. There is a growing interest in using the ground-based sky/cloud images for this purpose. Several methods exist that perform binary segmentation of clouds. In this paper, we propose to use a deep learning architecture (U-Net) to perform multi-label sky/cloud image segmentation. The proposed approach outperforms recent literature by a large margin. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/USNC-URSI.2019.8861850 | 2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium) |
Keywords | DocType | Volume |
multilabel cloud segmentation,deep network,cloud detection,deep learning architecture,ground-based sky-cloud images,U-Net,deep convolutional neural network | Journal | abs/1903.06562 |
ISSN | ISBN | Citations |
2572-3804 | 978-1-7281-0696-0 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumyabrata Dev | 1 | 62 | 13.94 |
| Shilpa Manandhar | 2 | 4 | 3.05 |
| Yee Hui Lee | 3 | 107 | 24.09 |
| Stefan Winkler | 4 | 216 | 21.60 |