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Feature selection using Kernel based Local Fisher Discriminant Analysis for hyperspectral image classification

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2 Author(s)
Guangyun Zhang ; School of Engineering and Information Technology, University College, Australian Defence Force Academy, University of New South Wales, Canberra, Australia ; Xiuping Jia

Feature extraction is an important research aspect for hyperspectral remote sensing image classification to reduce the complexity and improve the classification accuracy. In this paper, a new feature extraction method, Kernel based Local Fisher Discriminative Analysis (KLFDA), is applied to hyperspectral remote sensing processing. This method integrates the advantages of conventional supervised Fisher Discriminative Analysis and unsupervised Locality Preserving Projection methods. Several experiments using the real images have been conducted, which indicate a high efficiency of this algorithm for hyperspectral image classification.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Date of Conference:

24-29 July 2011