Feature extraction of hyperspectral images based on preserving neighborhood discriminant embedding

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.

[1]  D. H. Kim,et al.  Hyperspectral image processing using locally linear embedding , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[2]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[3]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[4]  J. Shan,et al.  Principal Component Analysis for Hyperspectral Image Classification , 2002 .

[5]  Joydeep Ghosh,et al.  Applying nonlinear manifold learning to hyperspectral data for land cover classification , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[6]  Tian Han,et al.  Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE) , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[7]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Shuicheng Yan,et al.  Graph embedding: a general framework for dimensionality reduction , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Thomas L. Ainsworth,et al.  Exploiting manifold geometry in hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Yousef Saad,et al.  Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[12]  Dong Guangjun,et al.  Dimensionality Reduction of Hyperspectral Data Based on ISOMAP Algorithm , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[13]  Qin Luo,et al.  Shrinkage-divergence-proximity locally linear embedding algorithm for dimensionality reduction of hyperspectral image , 2008 .

[14]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Guillermo Sapiro,et al.  Spatially Coherent Nonlinear Dimensionality Reduction and Segmentation of Hyperspectral Images , 2007, IEEE Geoscience and Remote Sensing Letters.

[16]  Guangyi Chen,et al.  A new nonlinear dimensionality reduction method with application to hyperspectral image analysis , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Guangyi Chen,et al.  Dimensionality reduction of hyperspectral imagery using improved locally linear embedding , 2007 .

[19]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.