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Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing 被引量：2

Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing
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作者 于钺 SunWeidong
机构地区 Department of Electronic Engineering China Ship Research and Development Academy
出处 《High Technology Letters》 EI CAS 2012年第4期333-342,共10页 高技术通讯（英文版）
基金 Supported by the National Natural Science Foundation of China （ No. 60872083 ） and the National High Technology Research and Development Program of China （No. 2007AA12Z149）.
关键词 非负矩阵分解 最小距离 高光谱数据 不混溶 高光谱遥感数据 混合像元分解 线性混合模型 欧几里德距离 hyperspectral data, nonnegative matrix factorization （NMF） , spectral unmixing,convex function, projected gradient （PG）
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TN911.22 [电子电信—通信与信息系统]
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  • 5Guilfoyle K J, Ahhouse M L, Chang C I. A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural net- works. IEEE Transactions on Geoscience and Remote Sens-irtg, 2001, 39(10): 2314-2318.
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  • 8Boardman J W. Automating spectral unmixing of AVIRIS data using convex geometry concepts. In: Proceedings of the Summaries of the 4th Annual JPL Airborne Geosci- ence Workshop, Washington, DC, USA, 1993. 11-14.
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High Technology Letters
2012年 第4期

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