Xue Shen, Wei Kong, Rujia Ma, Tao Chen, Ye Liu, Genghua Huang, Rong Shu
{"title":"基于点云过滤的激光雷达云层和气溶胶层探测方法","authors":"Xue Shen, Wei Kong, Rujia Ma, Tao Chen, Ye Liu, Genghua Huang, Rong Shu","doi":"10.1364/oe.536588","DOIUrl":null,"url":null,"abstract":"A point cloud filtering method is presented for atmospheric layer detection from lidar data. The method involves rising edge event recognition based on a wavelet transform function. Density-based clustering was then utilized to separate the real boundary from the original noisy point clouds based on continuous distribution characteristics of cloud and aerosol layer. Tests were carried out to verify the performance of our algorithm with synthetic lidar signals with noise. The layer base detection error within ± 5 bins was achieved for signals with SNRs higher than 3. Even for SNRs higher than 1, high consistency was still observed between retrieved results with our method and a visual analysis. These results indicate that our algorithm is suitable for unsupervised detection with large time-series datasets, such as CALIOP.","PeriodicalId":19691,"journal":{"name":"Optics express","volume":"184 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lidar cloud and aerosol layer detection method based on point cloud filtering\",\"authors\":\"Xue Shen, Wei Kong, Rujia Ma, Tao Chen, Ye Liu, Genghua Huang, Rong Shu\",\"doi\":\"10.1364/oe.536588\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A point cloud filtering method is presented for atmospheric layer detection from lidar data. The method involves rising edge event recognition based on a wavelet transform function. Density-based clustering was then utilized to separate the real boundary from the original noisy point clouds based on continuous distribution characteristics of cloud and aerosol layer. Tests were carried out to verify the performance of our algorithm with synthetic lidar signals with noise. The layer base detection error within ± 5 bins was achieved for signals with SNRs higher than 3. Even for SNRs higher than 1, high consistency was still observed between retrieved results with our method and a visual analysis. These results indicate that our algorithm is suitable for unsupervised detection with large time-series datasets, such as CALIOP.\",\"PeriodicalId\":19691,\"journal\":{\"name\":\"Optics express\",\"volume\":\"184 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics express\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1364/oe.536588\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics express","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1364/oe.536588","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
Lidar cloud and aerosol layer detection method based on point cloud filtering
A point cloud filtering method is presented for atmospheric layer detection from lidar data. The method involves rising edge event recognition based on a wavelet transform function. Density-based clustering was then utilized to separate the real boundary from the original noisy point clouds based on continuous distribution characteristics of cloud and aerosol layer. Tests were carried out to verify the performance of our algorithm with synthetic lidar signals with noise. The layer base detection error within ± 5 bins was achieved for signals with SNRs higher than 3. Even for SNRs higher than 1, high consistency was still observed between retrieved results with our method and a visual analysis. These results indicate that our algorithm is suitable for unsupervised detection with large time-series datasets, such as CALIOP.
期刊介绍:
Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.