{"title":"基于空间稀疏性的非负矩阵分解盲源分离多光谱图像解混方法","authors":"M. S. Karoui, Y. Deville, S. Hosseini, A. Ouamri","doi":"10.1109/IWECMS.2011.5952365","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an unsupervised spatial method in order to unmix each pixel of a remote sensing multispectral image. This method is related to the blind source separation (BSS) problem, and is based on sparse component analysis (SCA) and non-negative matrix factorization (NMF). Our approach consists in identifying the mixing matrix in the first stages, by using a spatial correlation-based SCA method, combined with clustering. An NMF method is used to extract spatial sources in the last stage. The overall proposed method is applicable to the globally underdetermined BSS model in multispectral remote sensing images. An experiment based on realistic synthetic mixtures is performed to evaluate the feasibility of the proposed approach. We also show that our method significantly outperforms the sequential maximum angle convex cone (SMACC) method.","PeriodicalId":211450,"journal":{"name":"2011 10th International Workshop on Electronics, Control, Measurement and Signals","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Spatial sparsity-based blind source separation method including non-negative matrix factorization for multispectral image unmixing\",\"authors\":\"M. S. Karoui, Y. Deville, S. Hosseini, A. Ouamri\",\"doi\":\"10.1109/IWECMS.2011.5952365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an unsupervised spatial method in order to unmix each pixel of a remote sensing multispectral image. This method is related to the blind source separation (BSS) problem, and is based on sparse component analysis (SCA) and non-negative matrix factorization (NMF). Our approach consists in identifying the mixing matrix in the first stages, by using a spatial correlation-based SCA method, combined with clustering. An NMF method is used to extract spatial sources in the last stage. The overall proposed method is applicable to the globally underdetermined BSS model in multispectral remote sensing images. An experiment based on realistic synthetic mixtures is performed to evaluate the feasibility of the proposed approach. We also show that our method significantly outperforms the sequential maximum angle convex cone (SMACC) method.\",\"PeriodicalId\":211450,\"journal\":{\"name\":\"2011 10th International Workshop on Electronics, Control, Measurement and Signals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 10th International Workshop on Electronics, Control, Measurement and Signals\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWECMS.2011.5952365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 10th International Workshop on Electronics, Control, Measurement and Signals","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWECMS.2011.5952365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial sparsity-based blind source separation method including non-negative matrix factorization for multispectral image unmixing
In this paper, we propose an unsupervised spatial method in order to unmix each pixel of a remote sensing multispectral image. This method is related to the blind source separation (BSS) problem, and is based on sparse component analysis (SCA) and non-negative matrix factorization (NMF). Our approach consists in identifying the mixing matrix in the first stages, by using a spatial correlation-based SCA method, combined with clustering. An NMF method is used to extract spatial sources in the last stage. The overall proposed method is applicable to the globally underdetermined BSS model in multispectral remote sensing images. An experiment based on realistic synthetic mixtures is performed to evaluate the feasibility of the proposed approach. We also show that our method significantly outperforms the sequential maximum angle convex cone (SMACC) method.