High-dimensional image data feature extraction by double discriminant embedding

We propose a supervised feature extraction method in this paper that uses two successive transformations to produce the extracted features. The first projection maximizes the difference between spectral features. Thus, produced features have minimum overlap in the new feature space. The second projection maximizes the discrimination between classes. The proposed method, which is called double discriminant embedding (DDE), uses just the first statistics of data. Thus, DDE has good efficiency using limited training samples. The experimental results on four popular hyperspectral images show the better efficiency of DDE in comparison with LDA, GDA, NWFE, and supervised LPP methods in small sample size situation.

[1]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[3]  Zheng Tian,et al.  Neighborhood Preserving Orthogonal PNMF Feature Extraction for Hyperspectral Image Classification , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Qi Wang,et al.  Hyperspectral Band Selection by Multitask Sparsity Pursuit , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Bor-Chen Kuo,et al.  Nonparametric weighted feature extraction for classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Ying-Nong Chen,et al.  Hyperspectral Image Classification Using Nearest Feature Line Embedding Approach , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Victor Haertel,et al.  Feature Extraction in Remote Sensing High-Dimensional Image Data , 2007, IEEE Geoscience and Remote Sensing Letters.

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

[9]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Liangpei Zhang,et al.  Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Maryam Imani,et al.  Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples , 2014, IEEE Geoscience and Remote Sensing Letters.

[13]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[14]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[15]  Chunhong Pan,et al.  Automatic Spatial–Spectral Feature Selection for Hyperspectral Image via Discriminative Sparse Multimodal Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Jiankun Hu,et al.  A New Dimensionality Reduction Algorithm for Hyperspectral Image Using Evolutionary Strategy , 2012, IEEE Transactions on Industrial Informatics.

[17]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Hyperspectral Image Segmentation Using S , 2022 .

[18]  Sri Ramakrishna,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[19]  Hassan Ghassemian,et al.  Linear Feature Extraction for Hyperspectral Images Based on Information Theoretic Learning , 2013, IEEE Geoscience and Remote Sensing Letters.

[20]  L. Ladha,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[21]  H. Ghassemian,et al.  Feature space discriminant analysis for hyperspectral data feature reduction , 2015 .

[22]  Peng Gong,et al.  Dimension Reduction of Hyperspectral Images for Classification Applications , 2002, Ann. GIS.

[23]  Xiuping Jia,et al.  Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction , 2012, IEEE Geoscience and Remote Sensing Letters.

[24]  Bor-Chen Kuo,et al.  Feature Mining for Hyperspectral Image Classification , 2013, Proceedings of the IEEE.

[25]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[26]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[27]  Lorenzo Bruzzone,et al.  A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[29]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .