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Indexed by:期刊论文

Date of Publication:2015-02-03

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:149

Issue:Part A

Page Number:65-70

ISSN No.:0925-2312

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

Abstract: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.