Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

被引：83

作者：

Harvey, NR ^{[1
]}

Theiler, J ^{[1
]}

Brumby, SP ^{[1
]}

Perkins, S ^{[1
]}

Szymanski, JJ ^{[1
]}

Bloch, JJ ^{[1
]}

Porter, RB ^{[1
]}

Galassi, M ^{[1
]}

Young, AC ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2002年 / 40卷 / 02期

关键词：

evolutionary algorithms; genetic programming; image processing; multispectral imagery; remote sensing; supervised classification;

D O I：

10.1109/36.992801

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：

We have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery.

引用

页码：393 / 404

页数：12

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