Name
Abstract | ||
|---|---|---|
We analyze clouds in the earth’s atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed, which use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this letter, we propose a lightweight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework and achieves state-of-the-art results on public databases. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LGRS.2019.2912140 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Clouds,Image segmentation,Image color analysis,Computer architecture,Cameras,Image resolution,Convolution | Cloud detection,Segmentation,Remote sensing,Fuzzy logic,Sky,Daytime,Image segmentation,Climatology,Cloud computing,Physics | Journal |
Volume | Issue | ISSN |
16 | 12 | 1545-598X |
Citations | PageRank | References |
2 | 0.43 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumyabrata Dev | 1 | 62 | 13.94 |
| Atul Nautiyal | 2 | 2 | 0.43 |
| Yee Hui Lee | 3 | 10 | 3.78 |
| Stefan Winkler | 4 | 53 | 5.56 |