{"title":"LWIR偏振图像距离不变异常检测","authors":"J. Romano, D. Rosario","doi":"10.1109/AIPR.2014.7041931","DOIUrl":null,"url":null,"abstract":"In this paper we present a modified version of a previously proposed anomaly detector for polarimetric imagery. This modified version is a more adaptive, range invariant anomaly detector based on the covariance difference test, the M-Box. The paper demonstrates the underlying issue of range to target dependency of the previous algorithm and offers a solution that is very easily implemented with the M-Box covariance test. Results are shown where the new algorithm is capable of identifying manmade objects as anomalies in both close and long range scenarios.","PeriodicalId":210982,"journal":{"name":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Range invariant anomaly detection for LWIR polarimetric imagery\",\"authors\":\"J. Romano, D. Rosario\",\"doi\":\"10.1109/AIPR.2014.7041931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a modified version of a previously proposed anomaly detector for polarimetric imagery. This modified version is a more adaptive, range invariant anomaly detector based on the covariance difference test, the M-Box. The paper demonstrates the underlying issue of range to target dependency of the previous algorithm and offers a solution that is very easily implemented with the M-Box covariance test. Results are shown where the new algorithm is capable of identifying manmade objects as anomalies in both close and long range scenarios.\",\"PeriodicalId\":210982,\"journal\":{\"name\":\"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"volume\":\"319 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2014.7041931\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2014.7041931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Range invariant anomaly detection for LWIR polarimetric imagery
In this paper we present a modified version of a previously proposed anomaly detector for polarimetric imagery. This modified version is a more adaptive, range invariant anomaly detector based on the covariance difference test, the M-Box. The paper demonstrates the underlying issue of range to target dependency of the previous algorithm and offers a solution that is very easily implemented with the M-Box covariance test. Results are shown where the new algorithm is capable of identifying manmade objects as anomalies in both close and long range scenarios.