Hyperspectral remote sensing image classification with information discriminative extreme learning machine

被引：7
|
作者：
Yan, Deqin [1 ]
Chu, Yonghe [1 ]
Li, Lina [1 ]
Liu, Deshan [1 ]
机构：
[1] Liaoning Normal Univ, Sch Comp & Informat Technol, Dalian 116081, Peoples R China
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2018年 / 77卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Extreme learning machine; Pattern recognition; Hyperspectral remote sensing image; Discrimination information; SUPPORT VECTOR MACHINES; NEAREST-NEIGHBOR; REPRESENTATION; REGRESSION;
D O I：
10.1007/s11042-017-4494-3
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Hyperspectral remote sensing image classification is important aspect of current research. Extreme learning machine (ELM) has been widely used in the field of pattern recognition for its efficient and good generalization performance. With the study of hyperspectral remote sensing image classification, this paper proposes an information discriminative extreme learning machine (IELM). IELM inherits the advantages of ELM, can solve the problems that ELM learning is insufficient for hyperspectral remote sensing image with limited scale of sample data. The proposed algorithm is tested by experiments for hyperspectral remote sensing image classification. The experiment results show that the proposed algorithm has better classification effect.
引用
收藏
页码：5803 / 5818
页数：16
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