Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines

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	Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines
	X. F. Li, X. P. Jia, L. G. Wang and K. Zhao
刊名	Ieee Geoscience and Remote Sensing Letters
	2016
卷号	13 期号:9 页码:1335-1339
通讯作者	赵凯
中文摘要	Several spectral unmixing techniques using multiple endmembers for each class have been developed. Although they can address within-class spectral variability, their unmixing results may have low unmixing resolution when the within-class variation is large due to the associated high uncertainty. Therefore, it is critical to represent data in an effective feature space so that the endmember classes are compact with small variation. In this letter, a minimum-class-variance support vector machine (MCVSVM) is further developed to extend its functions for both classification and spectral unmixing. Moreover, analytical expressions for spectral unmixing resolution (SUR) are provided to measure the spectral unmixing uncertainty in the new feature space. The extended MCVSVM (e_MCVSVM) can improve SUR and reduce the spectral unmixing uncertainty as it can effectively maximize the between-class scatter while minimizing the within-class scatter. Experimental results show that the e_MCVSVM algorithm performs better in terms of the unmixing accuracy and the computation speed compared with the other algorithms (e.g., fully constrained least squares and endmember bundles) in both linearly separable and nonseparable cases. This newly proposed approach advances the linear spectral mixture analysis with greater speed and higher accuracy based on the SVM after the SUR is effectively characterized.
内容类型	期刊论文
源URL	[http://159.226.123.10/handle/131322/7077]
专题	东北地理与农业生态研究所_湿地生态系统管理学科组_期刊论文
推荐引用方式 GB/T 7714	X. F. Li, X. P. Jia, L. G. Wang and K. Zhao. Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines[J]. Ieee Geoscience and Remote Sensing Letters,2016,13(9):1335-1339.
APA	X. F. Li, X. P. Jia, L. G. Wang and K. Zhao.(2016).Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines.Ieee Geoscience and Remote Sensing Letters,13(9),1335-1339.
MLA	X. F. Li, X. P. Jia, L. G. Wang and K. Zhao."Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines".Ieee Geoscience and Remote Sensing Letters 13.9(2016):1335-1339.