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Two-dimensional nearest neighbor classification for agricultural remote sensing

文献类型： 外文期刊

作者： Li, Xinrong 1 ; Tang, Yi 2 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Plant Nutr & Nat Resources, Beijing 100097, Peoples R China

2.Yunnan Univ Nationalities, Dept Math & Comp Sci, Kunming 650500, Yunnan, Peoples R China

关键词： Agricultural remote sensing;Matrix feature learning;Matrix dictionary learning;Discriminant dictionary;Hyperspectral image classification

期刊名称：NEUROCOMPUTING （ 影响因子：5.719； 五年影响因子：4.986 ）

ISSN： 0925-2312

年卷期： 2014 年 142 卷

页码：

收录情况： SCI

摘要： Two-dimensional nearest neighbor classification algorithm (2DNNC) is proposed for analyzing agriculture remote sensing data by combining matrix feature leaning and matrix-based dictionary learning. In the framework of 2DNNC, all hyperspectral feature vectors are transformed into matrix features by a set of nearest neighbor classifiers. The matrix features contain significantly discriminant information because of the label information from the set of nearest neighbor classifiers. Taking advantage of these matrix features, discriminant matrix dictionary is learned for classification by rank-1 matrices approximation. Experimental results on agriculture remote sensing data show the effectiveness and efficiency of the proposed algorithm on hyperspectral image classification. (C) 2014 Elsevier B.V. All rights reserved.

  • 相关文献

[1]Set-based similarity learning in subspace for agricultural remote sensing classification. Tang, Yi,Li, Xinrong.

[2]Bilateral filtering inspired locality preserving projections for hyperspectral images. Li, Xinrong,Pan, Jing,He, Yuqing,Liu, Changshu,Pan, Jing.

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