点击次数：

论文类型：期刊论文

发表时间：2015-02-03

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：149

期号：Part A

页面范围：65-70

ISSN号：0925-2312

关键字：Remote sensing classification; Nonnegative matrix factorization(NMF); Extreme learning machine (ELM); Ensemble learning; Feature extraction

摘要：There are only a few of labeled training samples in the remote sensing image classification. Therefore, it is a highly challenging problem that finds a good classification method which could achieve high accuracy to deal with these data. In this paper, we propose a new remote sensing image classification method based on extreme learning machine (ELM) ensemble. In order to promote the diversity within the ensemble, we adopt feature segmentation and then feature extraction with nonnegative matrix factorization (NMF) to the original data firstly. Then ELM is chosen as base classifier to improve the classification efficiency. The experimental results show that the proposed algorithm not only has high classification accuracy, but also handles the adverse impact of a few of labeled training samples in the classification of remote sensing image well both on the remote sensing image and UCI data. (C) 2014 Elsevier B.V. All rights reserved.