Parsimonious Gaussian Process Models for the Classification of Hyperspectral Remote Sensing Images

被引：4

作者：

Fauvel, Mathieu ^{[1
,2
]}

Bouveyron, Charles ^{[3
,4
]}

Girard, Stephane ^{[5
,6
]}

机构：

[1] UMR 1201 DYNAFOR INRA, F-31326 Toulouse, France

[2] Inst Natl Polytech Toulouse, F-31029 Toulouse, France

[3] Univ Paris 05, Lab MAP5, UMR CNRS 8145, F-75270 Paris, France

[4] Sorbonne Paris Cite, F-75270 Paris, France

[5] INRIA Grenoble Rhone Alpes, Team MISTIS, F-38330 Montbonnot St Martin, France

[6] LJK, F-38330 Montbonnot St Martin, France

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2015年 / 12卷 / 12期

关键词：

Classification; hyperspectral; kernel methods; parsimonious Gaussian process; remote sensing images; DISCRIMINANT-ANALYSIS;

D O I：

10.1109/LGRS.2015.2481321

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：

A family of parsimonious Gaussian process models for classification is proposed in this letter. A subspace assumption is used to build these models in the kernel feature space. By constraining some parameters of the models to be common between classes, parsimony is controlled. Experimental results are given for three real hyperspectral data sets, and comparisons are done with three other classifiers. The proposed models show good results in terms of classification accuracy and processing time.

引用

页码：2423 / 2427

页数：5

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