Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data
| Autor: | Zhonghua Hong, Jiwei Yin, Yanling Han, Wanting Meng, Yun Zhang, Xu Chen, Shuhu Yang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
DDM
Mean squared error GNSS-R Computer science wind direction CYGNSS SVM Science Satellite system Wind direction Wind speed Support vector machine Feature (computer vision) Physics::Space Physics Hyperparameter optimization General Earth and Planetary Sciences Satellite Physics::Atmospheric and Oceanic Physics Remote sensing |
| Zdroj: | Remote Sensing, Vol 13, Iss 4451, p 4451 (2021) Remote Sensing; Volume 13; Issue 21; Pages: 4451 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13214451 |
| Popis: | In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |