Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experiment, kernel principal components are used to construct the extended morphological profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. For the one data set, the overall classification accuracy increases from 79% to 96% with the proposed approach.

[1]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[2]  Jie Yang,et al.  Modified Kernel Functions by Geodesic Distance , 2004, EURASIP J. Adv. Signal Process..

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

[4]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Curt H. Davis,et al.  Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information , 2005, EURASIP J. Adv. Signal Process..

[6]  Marc Lennon.  Methodes d'analyse d'images hyperspectrales : exploitation du capteur aéroporté CASI pour des applications de cartographie agro-environnementale en Bretagne , 2002 .

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

[8]  Thomas G. Dietterich,et al.  Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA , 2001, NIPS.

[9]  J.A. Benediktsson,et al.  Kernel Principal Component Analysis for Feature Reduction in Hyperspectrale Images Analysis , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[10]  Pavel Paclík,et al.  Adaptive floating search methods in feature selection , 1999, Pattern Recognit. Lett..

[11]  David A. Landgrebe,et al.  Analyzing high-dimensional multispectral data , 1993, IEEE Trans. Geosci. Remote. Sens..

[12]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Lawrence K. Saul,et al.  Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech , 2000, NIPS.

[14]  Mathieu Fauvel,et al.  Spectral and Spatial Methods for the Classification of Urban Remote Sensing Data , 2007 .

[15]  Tan Yee Fan,et al.  A Tutorial on Support Vector Machine , 2009 .

[16]  N. Keshava,et al.  Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[18]  N. Aronszajn.  Theory of Reproducing Kernels. , 1950 .

[19]  Jon Atli Benediktsson,et al.  Decision Fusion for Hyperspectral Classification , 2006 .

[20]  Marc Lennon,et al.  Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images , 2002, SPIE Remote Sensing.

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

[22]  Gunnar Rätsch,et al.  Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[23]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003 .

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

[25]  Antonio J. Plaza,et al.  Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[27]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[28]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[30]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[31]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[32]  Jean-Baptiste Mouret,et al.  Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields , 2004, EURASIP J. Adv. Signal Process..

[33]  L. Journaux,et al.  Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[34]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Heesung Kwon,et al.  A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery , 2007, EURASIP J. Adv. Signal Process..

[36]  Jon Atli Benediktsson,et al.  Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[37]  Kim L. Boyer,et al.  Linearized vegetation indices based on a formal statistical framework , 2004, IEEE Transactions on Geoscience and Remote Sensing.

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

[39]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[40]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[41]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

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

[43]  Kyoung-Su Park,et al.  Spectral Content Characterization for Efficient Image Detection Algorithm Design , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[44]  Johannes R. Sveinsson,et al.  Fusion of Morphological and Spectral Information for Classification of Hyperspectal Urban Remote Sensing Data , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[45]  David A. Landgrebe,et al.  Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[46]  Lorenzo Bruzzone,et al.  A new search algorithm for feature selection in hyperspectral remote sensing images , 2001, IEEE Trans. Geosci. Remote. Sens..

[47]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[48]  Bing Zhang,et al.  Classification of hyperspectral data in urban areas , 1998, Asia-Pacific Environmental Remote Sensing.

[49]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .