{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:56:35Z","timestamp":1760144195531,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Shandong Province","award":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"],"award-info":[{"award-number":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"]}]},{"name":"the National Key R&amp;D Program of China","award":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"],"award-info":[{"award-number":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"]}]},{"name":"Qing Lan Project of Jiangsu Province","award":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"],"award-info":[{"award-number":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"]}]},{"name":"the Natural Science Foundation of China","award":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"],"award-info":[{"award-number":["ZR2023MD012","2017YFC1501505","R2023Q07","62033010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning\u2019s time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the \u201clightning\u201d and \u201cnon-lightning\u201d samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved.<\/jats:p>","DOI":"10.3390\/rs16071151","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T11:05:33Z","timestamp":1711451133000},"page":"1151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lightning Optical Automatic Detection Method Based on a Deep Neural Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4039-6727","authenticated-orcid":false,"given":"Jialei","family":"Wang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4351-8629","authenticated-orcid":false,"given":"Lin","family":"Song","sequence":"additional","affiliation":[{"name":"Qingdao Ecological and Agricultural Meteorological Center, Qingdao 266003, China"}]},{"given":"Qilin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Quanbo","family":"Ge","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Shengye","family":"Yan","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Gaofeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yuqing","family":"Zhong","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Qingda","family":"Li","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather\/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qie, X., and Kong, X. 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