Name
Abstract | ||
|---|---|---|
Ground-based whole sky cameras have opened up new opportunities for monitoring the earthu0027s atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole sky imagers can have high spatial and temporal resolution, which is an important pre-requisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, etc. Extracting valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine learning techniques have become available to aid with the image analysis. This article provides a detailed walk-through of recent developments in these techniques and their applications in ground-based imaging. We aim to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applications -- segmentation, classification, and denoising. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Noise reduction,Computer vision,Satellite,Weather prediction,Cloud attenuation,Computer science,Segmentation,Sky,Artificial intelligence,Temporal resolution,Machine learning |
DocType | Volume | Citations |
Journal | abs/1606.02811 | 0 |
PageRank | References | Authors |
0.34 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumyabrata Dev | 1 | 62 | 13.94 |
| Bihan Wen | 2 | 225 | 18.64 |
| Yee Hui Lee | 3 | 107 | 24.09 |
| Stefan Winkler | 4 | 216 | 21.60 |