Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

被引：44

作者：

Chi, Mingmin ^{[1
]}

Kun, Qian ^{[1
]}

Benediktsson, Jon Atli ^{[2
]}

Feng, Rui ^{[1
]}

机构：

[1] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China

[2] Univ Iceland, Fac Elect & Comp Engn, IS-107 Reykjavik, Iceland

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2009年 / 6卷 / 04期

关键词：

Ensemble classification; hyperspectral remote sensing images; mixture of Gaussians (MoGs); support cluster machine (SCM);

D O I：

10.1109/LGRS.2009.2024624

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.

引用

页码：762 / 766

页数：5

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