Classification of Biological Scatters Using Polarimetric Weather Radar
Weather radar holds the capability to monitor the extensive migration of bird and insect species. In particular, polarimetric weather radar can enhance aerial ecological monitoring by quantifying target shape through the measurement of polarization moments. This article introduces an intelligent alg...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10475422/ |
| _version_ | 1827289998768996352 |
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| author | Cheng Hu Zhuoran Sun Kai Cui Huafeng Mao Rui Wang Xiao Kou Dongli Wu Fan Xia |
| author_facet | Cheng Hu Zhuoran Sun Kai Cui Huafeng Mao Rui Wang Xiao Kou Dongli Wu Fan Xia |
| author_sort | Cheng Hu |
| collection | DOAJ |
| description | Weather radar holds the capability to monitor the extensive migration of bird and insect species. In particular, polarimetric weather radar can enhance aerial ecological monitoring by quantifying target shape through the measurement of polarization moments. This article introduces an intelligent algorithm to classify bird and insect migration using polarimetric weather radar data. A radar image dataset was formed by intentionally curating typical migratory data of birds and insects captured by the polarimetric weather radar. Next, point features and spatial texture features were extracted from the radar images in the dataset for training a classifier using a supervised learning approach, resulting in a classification accuracy of 93.56%. Furthermore, the importance of the features was analyzed, uncovering that the most influential attribute was the reflectivity factor at 33.83%, surpassing the cumulative influence of other dual-polarization moments. In addition, spatial textures also played an essential role for the classifier, collectively weighing 35.65%. Lastly, the proposed method was validated with bird radar data, attaining an accuracy level of 95.36%. |
| first_indexed | 2024-04-24T12:00:37Z |
| format | Article |
| id | doaj.art-3385461843e240f3a73f3824cf079a4d |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-24T12:00:37Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-3385461843e240f3a73f3824cf079a4d2024-04-08T23:00:10ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01177436744710.1109/JSTARS.2024.337880110475422Classification of Biological Scatters Using Polarimetric Weather RadarCheng Hu0https://orcid.org/0000-0001-7582-5291Zhuoran Sun1https://orcid.org/0000-0001-6354-1267Kai Cui2https://orcid.org/0000-0002-7208-9046Huafeng Mao3https://orcid.org/0000-0002-0190-8534Rui Wang4https://orcid.org/0000-0002-3510-7356Xiao Kou5Dongli Wu6Fan Xia7Radar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaRadar Technology Research Institute and School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaMeteorological Observation Center of China Meteorological Administration, Beijing, ChinaKey Laboratory for Meteorological Disaster Prevention and Mitigation of Shandong Province, Jinan, ChinaWeather radar holds the capability to monitor the extensive migration of bird and insect species. In particular, polarimetric weather radar can enhance aerial ecological monitoring by quantifying target shape through the measurement of polarization moments. This article introduces an intelligent algorithm to classify bird and insect migration using polarimetric weather radar data. A radar image dataset was formed by intentionally curating typical migratory data of birds and insects captured by the polarimetric weather radar. Next, point features and spatial texture features were extracted from the radar images in the dataset for training a classifier using a supervised learning approach, resulting in a classification accuracy of 93.56%. Furthermore, the importance of the features was analyzed, uncovering that the most influential attribute was the reflectivity factor at 33.83%, surpassing the cumulative influence of other dual-polarization moments. In addition, spatial textures also played an essential role for the classifier, collectively weighing 35.65%. Lastly, the proposed method was validated with bird radar data, attaining an accuracy level of 95.36%.https://ieeexplore.ieee.org/document/10475422/Biological scatters classificationbird migrationbird radarpolarimetric weather radarrandom forest |
| spellingShingle | Cheng Hu Zhuoran Sun Kai Cui Huafeng Mao Rui Wang Xiao Kou Dongli Wu Fan Xia Classification of Biological Scatters Using Polarimetric Weather Radar IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Biological scatters classification bird migration bird radar polarimetric weather radar random forest |
| title | Classification of Biological Scatters Using Polarimetric Weather Radar |
| title_full | Classification of Biological Scatters Using Polarimetric Weather Radar |
| title_fullStr | Classification of Biological Scatters Using Polarimetric Weather Radar |
| title_full_unstemmed | Classification of Biological Scatters Using Polarimetric Weather Radar |
| title_short | Classification of Biological Scatters Using Polarimetric Weather Radar |
| title_sort | classification of biological scatters using polarimetric weather radar |
| topic | Biological scatters classification bird migration bird radar polarimetric weather radar random forest |
| url | https://ieeexplore.ieee.org/document/10475422/ |
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