Academic Journal
A new method based on stacked auto-encoders to identify abnormal weather radar echo images
| العنوان: | A new method based on stacked auto-encoders to identify abnormal weather radar echo images |
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| المؤلفون: | Ling Yang, Yun Wang, Zhongke Wang, Yang Qi, Yong Li, Zhipeng Yang, Wenle Chen |
| المصدر: | EURASIP Journal on Wireless Communications and Networking, Vol 2020, Iss 1, Pp 1-15 (2020) |
| بيانات النشر: | SpringerOpen, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Telecommunication LCC:Electronics |
| مصطلحات موضوعية: | Radar echo image, Coordinate transformation, Integration projection, Deep learning, Recognition, Telecommunication, TK5101-6720, Electronics, TK7800-8360 |
| الوصف: | Abstract It is not denied that real-time monitoring of radar products is an important part in actual meteorological operations. But the weather radar often brings out abnormal radar echoes due to various factors, such as climate and hardware failure. So it is of great practical significance and research value to realize automatic identification of radar anomaly products. However, the traditional algorithms to identify anomalies of weather radar echo images are not the most accurate and efficient. In order to improve the efficiency of the anomaly identification, a novel method combining the theory of classical image processing and deep learning was proposed. The proposed method mainly includes three parts: coordinate transformation, integral projection, and classification using deep learning. Furthermore, extensive experiments have been done to validate the performance of the new algorithm. The results show that the recognition rate of the proposed method can reach up to more than 95%, which can successfully achieve the goal of screening abnormal radar echo images; also, the computation speed of it is fairly satisfactory. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1687-1499 |
| Relation: | http://link.springer.com/article/10.1186/s13638-020-01769-3; https://doaj.org/toc/1687-1499 |
| DOI: | 10.1186/s13638-020-01769-3 |
| URL الوصول: | https://doaj.org/article/900df03f95ec4cb7927cbe9506b3738b |
| رقم الانضمام: | edsdoj.900df03f95ec4cb7927cbe9506b3738b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16871499 |
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| DOI: | 10.1186/s13638-020-01769-3 |