Name
Title | ||
|---|---|---|
Deep feature extraction and its application for hailstorm detection in a large collection of radar images. |
Abstract | ||
|---|---|---|
With the improvement of sensing and storing technologies, a large amount of weather data become available, and the data size will continue growing as radar imaging instruments continuously acquire data. In this work, we develop a deep convolutional neural network with a large collection of radar images as input to train and validate a classification model, and then we use the model to detect hailstorm events. This is interdisciplinary work between the disciplines of computer science and meteorology. We are primarily interested in what hailstorm features the network learns and how it learns as convolving into deeper iterations. The evaluation results show a high classification accuracy in comparison with existing hailstorm detection approaches. The proposed approach can also be used to detect other types of severe weather events with minimal efforts on variable or parameter changes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11760-018-1380-z | Signal, Image and Video Processing |
Keywords | Field | DocType |
Hailstorm detection, Convolutional neural network, Deep feature extraction | Radar imaging,Pattern recognition,Convolutional neural network,Severe weather,Feature extraction,Artificial intelligence,Weather data,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 3 | 1863-1711 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iksha Gurung | 1 | 0 | 2.37 |
| Chao Peng | 2 | 29 | 4.43 |
| Manil Maskey | 3 | 31 | 12.02 |
| Rahul Ramachandran | 4 | 117 | 29.54 |