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Feature extraction of hyperspectral images based on preserving neighborhood discriminant embedding

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4 Author(s)
Jinhuan Wen ; Sch. of Sci., Northwestern Polytech. Univ., Xi''an, China ; Zheng Tian ; Hongwei She ; Weidong Yan

A novel manifold learning feature extraction approach-preserving neighborhood discriminant embedding (PNDE) of hyperspectral image is proposed in this paper. The local geometrical and discriminant structure of the data manifold can be accurately characterized by within-class neighboring graph and between-class neighboring graph. Unlike manifold learning, such as LLE, Isomap and LE, which cannot deal with new test samples and images larger than 70×70, the method here can process full scene hyperspectral images. Experiments results on hyperspectral datasets and real-word datasets show that the proposed method can efficiently reduce the dimensionality while maintaining high classification accuracy. In addition, only a small amount of training samples are needed.

Published in:

Image Analysis and Signal Processing (IASP), 2010 International Conference on

Date of Conference:

9-11 April 2010