{"title":"基于交叉引用的多传感器问题计算多核方法","authors":"W. Case, B. Bertram","doi":"10.1109/CAMAP.2005.1574185","DOIUrl":null,"url":null,"abstract":"This paper attempts to show how multi-sensor imagery data may be used to generate image reconstructions that are superior to any produced from individual sensors, and to provide a plausibility argument as to why this comes about. The case of two sensors will be considered for convenience and brevity.","PeriodicalId":281761,"journal":{"name":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","volume":"-1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A computational multi-kernel approach to multi-sensor problems using cross-referencing (CREF)\",\"authors\":\"W. Case, B. Bertram\",\"doi\":\"10.1109/CAMAP.2005.1574185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper attempts to show how multi-sensor imagery data may be used to generate image reconstructions that are superior to any produced from individual sensors, and to provide a plausibility argument as to why this comes about. The case of two sensors will be considered for convenience and brevity.\",\"PeriodicalId\":281761,\"journal\":{\"name\":\"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.\",\"volume\":\"-1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMAP.2005.1574185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMAP.2005.1574185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A computational multi-kernel approach to multi-sensor problems using cross-referencing (CREF)
This paper attempts to show how multi-sensor imagery data may be used to generate image reconstructions that are superior to any produced from individual sensors, and to provide a plausibility argument as to why this comes about. The case of two sensors will be considered for convenience and brevity.