{"title":"多光谱图像比较的可视化分析","authors":"Guozheng Li, Shuai Chen, Qiusheng Li, Zhibang Jiang, Yuening Shi, Qiangqiang Liu, Xi Liu, Xiaoru Yuan","doi":"10.1109/VAST.2017.8585456","DOIUrl":null,"url":null,"abstract":"The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visual Analysis for Multi-Spectral Images Comparisons\",\"authors\":\"Guozheng Li, Shuai Chen, Qiusheng Li, Zhibang Jiang, Yuening Shi, Qiangqiang Liu, Xi Liu, Xiaoru Yuan\",\"doi\":\"10.1109/VAST.2017.8585456\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system.\",\"PeriodicalId\":149607,\"journal\":{\"name\":\"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VAST.2017.8585456\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VAST.2017.8585456","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual Analysis for Multi-Spectral Images Comparisons
The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system.
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