A Hyper-spectral Image Classification Algorithms Based on QuickNon-negative Matrix Factorization and RBF Neural Network

doi:10.11873/j.issn.1004-0323.2009.3.385

Remote Sensing Technology and Application

2009, Vol. 24

Issue (3): 385-390 DOI: 10.11873/j.issn.1004-0323.2009.3.385

A Hyper-spectral Image Classification Algorithms Based on QuickNon-negative Matrix Factorization and RBF Neural Network

DI Wen-yu,HE Ming-yi,MEI Shao-hui

（School of Electronics and Information|Northwestern Polytechnical University| Xi'an 710072 China）

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Abstract

A new method combined Non-negative Matrix Factorization (NMF) with Projected Gradient (PG) is proposed for hyper-spectral image classification.Projected Gradient method demonstrates much faster convergence than the popular multiplicative update approach in the iteration process of two sub-problems from NMF thus effectively maintains higher classification accuracy than traditional methods; RBF neural network achieves higher accuracy and faster classification process compared to BP network.The new method combines the advantages of the above two,applying PGNMF for feature extraction and RBFNN as classifier.The experiment shows that compared to traditional NMF and PCA,PGNMF-RBF has higher accuracy for classification and less time consumption.The classification accuracy for 6 classes reaches 83.34%.This paper demonstrates PGNMF-RBF an effective and promising method in hyper-spectral image classification.

Key words: Projected gradient Non-negative matrix factorization RBF neural network Image classification

Received: 27 December 2008 Published: 20 January 2010

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Cite this article:

DI Wen-yu,HE Ming-yi,MEI Shao-hui. A Hyper-spectral Image Classification Algorithms Based on QuickNon-negative Matrix Factorization and RBF Neural Network. Remote Sensing Technology and Application, 2009, 24(3): 385-390.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2009.3.385 OR http://www.rsta.ac.cn/EN/Y2009/V24/I3/385

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