Hyperspectral image classification based on Multiple Improved particle swarm cooperative optimization and SVM

Yuemei Ren, Yanning Zhang, Qingjie Meng, Lei Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

The huge increase of hyperspectral data dimensionality and information redundancy has brought high computational cost as well as the over-fitting risk of classification. In this paper, we present an automatic band selection and classification method based on a novel wrapper Multiple Improved particle swarm cooperative optimization and support vector machine model (MIPSO-SVM). The MIPSO-SVM model optimizes both the band subset and SVM kernel parameters simultaneously. In the proposed model, the particle swarm is divided into two sub-swarms. And PSO is improved firstly, by the new update strategy of position and velocity. Then the sub-swarms perform the improved PSO (IPSO) for band selection and classifier parameters optimization independently. Finally, in the process of cooperative evolution, extremal optimization (EO) is incorporated to maintain the diversity of swarms and enhance the space exploration ability of the proposed model. Experimental results demonstrate the effectiveness of the proposed method for band selection and classification of hyperspectral images.

源语言英语
主期刊名ICPR 2012 - 21st International Conference on Pattern Recognition
2274-2277
页数4
出版状态已出版 - 2012
活动21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, 日本
期限: 11 11月 201215 11月 2012

出版系列

姓名Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会议

会议21st International Conference on Pattern Recognition, ICPR 2012
国家/地区日本
Tsukuba
时期11/11/1215/11/12

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