Risheng Huang, C. Xia, Shuhan Chen, Liaoying Zhao, Xiaorun Li
{"title":"基于半二次型鲁棒高光谱解混框架","authors":"Risheng Huang, C. Xia, Shuhan Chen, Liaoying Zhao, Xiaorun Li","doi":"10.1117/12.2665465","DOIUrl":null,"url":null,"abstract":"We present a general half-quadratic based hyperspectral unmixing (HU) framework to solve the robust or sparse unmixing problem. A series of potential methods can be designed and developed to solve HU problem through this framework. By introducing correntropy metric, a correntropy based spatial-spectral robust sparsity regularized (CSsRS-NMF) unmixing method is derived through the proposed framework to achieve two-dimensional robustness and adaptive weighted sparsity constraint for abundances simultaneously.","PeriodicalId":258680,"journal":{"name":"Earth and Space From Infrared to Terahertz (ESIT 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Half-quadratic based robust hyperspectral unmixing framework\",\"authors\":\"Risheng Huang, C. Xia, Shuhan Chen, Liaoying Zhao, Xiaorun Li\",\"doi\":\"10.1117/12.2665465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a general half-quadratic based hyperspectral unmixing (HU) framework to solve the robust or sparse unmixing problem. A series of potential methods can be designed and developed to solve HU problem through this framework. By introducing correntropy metric, a correntropy based spatial-spectral robust sparsity regularized (CSsRS-NMF) unmixing method is derived through the proposed framework to achieve two-dimensional robustness and adaptive weighted sparsity constraint for abundances simultaneously.\",\"PeriodicalId\":258680,\"journal\":{\"name\":\"Earth and Space From Infrared to Terahertz (ESIT 2022)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth and Space From Infrared to Terahertz (ESIT 2022)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2665465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space From Infrared to Terahertz (ESIT 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2665465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Half-quadratic based robust hyperspectral unmixing framework
We present a general half-quadratic based hyperspectral unmixing (HU) framework to solve the robust or sparse unmixing problem. A series of potential methods can be designed and developed to solve HU problem through this framework. By introducing correntropy metric, a correntropy based spatial-spectral robust sparsity regularized (CSsRS-NMF) unmixing method is derived through the proposed framework to achieve two-dimensional robustness and adaptive weighted sparsity constraint for abundances simultaneously.