UNSUPERVISED NONLINEAR FEATURE EXTRACTION METHOD AND ITS EFFECTS ON TARGET DETECTION IN HIGH-DIMENSIONAL DATA

The principal component analysis (PCA) is one of the most effective unsupervised techniques for feature extraction. To extract higher order properties of data, researchers extended PCA to kernel PCA (KPCA) by means of kernel machines. In this paper, KPCA is applied as a feature extraction procedure to dimension reduction for target detection as a preprocessing on hyperspectral images. Then the detection was done with a support vector data description (SVDD) algorithm which is another type of one-class support vector machines (SVMs). The SVDD constructs a minimum hypersphere enclosing the target objects as much as possible. For the supervised learning, SVDD has been trained with a training set which has been chosen from target class. Balanced classification rate (BCR) and F-measure have been used to evaluate the performance of proposed technique against full-band target detection. The experimental results on hyperspectral data from the HYDICE sensors show that the KPCA based dimension reduction offers high performance for target detection applications by SVDD. Index Terms—Feature extraction, hyperspectral imagery, kernel principal component analysis, support vector data description, target detection.

[1]  Pierre Comon,et al.  Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast With Algebraic Optimal Step Size , 2010, IEEE Transactions on Neural Networks.

[2]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[3]  Qiguang Miao,et al.  A novel image fusion method using WBCT and PCA , 2008 .

[4]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[5]  David W. Scott,et al.  The Curse of Dimensionality and Dimension Reduction , 2008 .

[6]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

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

[8]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[9]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[10]  Amit Banerjee,et al.  A support vector method for anomaly detection in hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Wei-Cheng Chang.  A Revisit to Support Vector Data Description ( SVDD ) , 2013 .

[12]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[13]  Lorenzo Bruzzone,et al.  Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Chein-I. Chang.  Hyperspectral Data Exploitation: Theory and Applications , 2007 .

[15]  Peijun Du,et al.  A NEW BAND SELECTION ALGORITHM FOR HYPERSPECTRAL DATA BASED ON FRACTAL DIMENSION , 2008 .

[16]  Amit Banerjee,et al.  Fast Hyperspectral Anomaly Detection via SVDD , 2007, 2007 IEEE International Conference on Image Processing.

[17]  Lorenzo Bruzzone,et al.  A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.