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论文类型：期刊论文

发表时间：2012-02-15

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：78

期号：1,SI

页面范围：133-138

ISSN号：0925-2312

关键字：DECORATE; Rotation Forest; RBFNN; Hybrid algorithm

摘要：The amounts and types of remote sensing data have increased rapidly, and the classification of these datasets has become more and more overwhelming for a single classifier in practical applications. In this paper, an ensemble algorithm based on Diversity Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATEs) and Rotation Forest is proposed to solve the classification problem of remote sensing image. In this ensemble algorithm, the RBF neural networks are employed as base classifiers. Furthermore, interpolation technology for identical distribution is used to remold the input datasets. These remolded datasets will construct new classifiers besides the initial classifiers constructed by the Rotation Forest algorithm. The change of classification error is used to decide whether to add another new classifier. Therefore, the diversity among these classifiers will be enhanced and the accuracy of classification will be improved. Adaptability of the proposed algorithm is verified in experiments implemented on standard datasets and actual remote sensing dataset. (C) 2011 Elsevier B.V. All rights reserved.