A remote sensing image classification method based on extreme learning machine ensemble

Authors:
Min Han;Ben Liu
Affiliations:
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

There are few 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 and strong generalization to deal with those 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 do feature segmentation and 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 few training samples in the classification of remote sensing well both on the remote sensing image and UCI data.