Jarrod P. Brown, Christian L. Saludez, Darrell Card, Rodney G. Roberts
{"title":"材料分类的偏振激光雷达特征选择","authors":"Jarrod P. Brown, Christian L. Saludez, Darrell Card, Rodney G. Roberts","doi":"10.1109/RAPID49481.2020.9195711","DOIUrl":null,"url":null,"abstract":"Feature selection for polarimetric lidar material classification is explored. Experimental measurements of a diverse sample set are collected and common features emphasizing polarimetric and polarization-insensitive reflectance are calculated. Multiple classifiers are implemented. Results suggest two transmit and two return polarization states maximize classification performance.","PeriodicalId":220244,"journal":{"name":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Polarimetric Lidar Feature Selection for Material Classification\",\"authors\":\"Jarrod P. Brown, Christian L. Saludez, Darrell Card, Rodney G. Roberts\",\"doi\":\"10.1109/RAPID49481.2020.9195711\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Feature selection for polarimetric lidar material classification is explored. Experimental measurements of a diverse sample set are collected and common features emphasizing polarimetric and polarization-insensitive reflectance are calculated. Multiple classifiers are implemented. Results suggest two transmit and two return polarization states maximize classification performance.\",\"PeriodicalId\":220244,\"journal\":{\"name\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAPID49481.2020.9195711\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAPID49481.2020.9195711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Polarimetric Lidar Feature Selection for Material Classification
Feature selection for polarimetric lidar material classification is explored. Experimental measurements of a diverse sample set are collected and common features emphasizing polarimetric and polarization-insensitive reflectance are calculated. Multiple classifiers are implemented. Results suggest two transmit and two return polarization states maximize classification performance.
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