Fast k-NN classification using the cluster-space approach

被引：26

作者：

Jia, XP ^{[1
]}

Richards, JA ^{[1
]}

机构：

[1] Australian Natl Univ, Res Sch Informat Sci & Engn, Canberra, ACT 0200, Australia

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2005年 / 2卷 / 02期

关键词：

classification; cluster-space representation; hyperspectral; k-NN;

D O I：

10.1109/LGRS.2005.846437

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 fast kappa-nearest neighbor algorithm is presented which combines kappa-NN with a cluster-space data representation. Implementation of the algorithm is easier, and classification time can be significantly reduced. Computer-generated data show the modified kappa-NN retains the advantage of nonparametric analysis but with significant reduction in computational load. Results from tests carried out with Hyperion data demonstrate that the simplification has little effect on classification performance, and yet efficiency is greatly improved.

引用

页码：225 / 228

页数：4

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