Weiwei Sun, Chun Liu, Yan Xu, Long Tian, Weiyue Li
{"title":"基于波段加权支持向量机的高光谱图像分类方法","authors":"Weiwei Sun, Chun Liu, Yan Xu, Long Tian, Weiyue Li","doi":"10.1109/LGRS.2017.2729940","DOIUrl":null,"url":null,"abstract":"A band-weighted support vector machine (BWSVM) method is proposed to classify hyperspectral imagery (HSI). The BWSVM presents an L1 penalty term of band weight vector to regularize the regular SVM model. The L1 norm regularization term guarantees the sparsity of band weights and describes potentially divergent contributions from different bands in modeling the binary SVM model. The BWSVM adopts the KerNel iterative feature extraction algorithm to minimize the nonconvex program. It linearizes nonlinear kernels and iteratively optimizes two convex subproblems with respect to both sample coefficients and band weights. The class label is determined by picking the largest sample coefficients from all its binary models of BWSVM. Two popular HSI data sets are utilized to testify the classification performance of BWSVM. Experimental results show that the BWSVM outperforms three state-of-the-art classifiers including SVM, random forest, and k-nearest neighbor.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"14 1","pages":"1710-1714"},"PeriodicalIF":4.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2017.2729940","citationCount":"22","resultStr":"{\"title\":\"A Band-Weighted Support Vector Machine Method for Hyperspectral Imagery Classification\",\"authors\":\"Weiwei Sun, Chun Liu, Yan Xu, Long Tian, Weiyue Li\",\"doi\":\"10.1109/LGRS.2017.2729940\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A band-weighted support vector machine (BWSVM) method is proposed to classify hyperspectral imagery (HSI). The BWSVM presents an L1 penalty term of band weight vector to regularize the regular SVM model. The L1 norm regularization term guarantees the sparsity of band weights and describes potentially divergent contributions from different bands in modeling the binary SVM model. The BWSVM adopts the KerNel iterative feature extraction algorithm to minimize the nonconvex program. It linearizes nonlinear kernels and iteratively optimizes two convex subproblems with respect to both sample coefficients and band weights. The class label is determined by picking the largest sample coefficients from all its binary models of BWSVM. Two popular HSI data sets are utilized to testify the classification performance of BWSVM. Experimental results show that the BWSVM outperforms three state-of-the-art classifiers including SVM, random forest, and k-nearest neighbor.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"14 1\",\"pages\":\"1710-1714\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/LGRS.2017.2729940\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/LGRS.2017.2729940\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/LGRS.2017.2729940","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Band-Weighted Support Vector Machine Method for Hyperspectral Imagery Classification
A band-weighted support vector machine (BWSVM) method is proposed to classify hyperspectral imagery (HSI). The BWSVM presents an L1 penalty term of band weight vector to regularize the regular SVM model. The L1 norm regularization term guarantees the sparsity of band weights and describes potentially divergent contributions from different bands in modeling the binary SVM model. The BWSVM adopts the KerNel iterative feature extraction algorithm to minimize the nonconvex program. It linearizes nonlinear kernels and iteratively optimizes two convex subproblems with respect to both sample coefficients and band weights. The class label is determined by picking the largest sample coefficients from all its binary models of BWSVM. Two popular HSI data sets are utilized to testify the classification performance of BWSVM. Experimental results show that the BWSVM outperforms three state-of-the-art classifiers including SVM, random forest, and k-nearest neighbor.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.