Incorporating spatial properties in subspace detection

The aim of this analysis is to develop a subspace detection technique using a hybrid approach which combines nonlinear feature extraction and feature selection for the task of hyperspectral image classification. In the proposed approach Kernel Principal Component Analysis (KPCA) is applied at the first step to generate the new features from the original data. Then pixel based spatial correlation is measured for each of the KPCA images to rank them based on their spatial objects/contents. These KPCA and spatial correlation based ranking scores are combined to obtain an informative subset of features. The experimental analysis conducted on a real hyperspectral image acquired by the AVIRIS sensor shows the advantage of the proposed approach in terms of classification accuracy.

[1]  Ralph Bernstein,et al.  Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[2]  David A. Landgrebe,et al.  Covariance estimation with limited training samples , 1999, IEEE Trans. Geosci. Remote. Sens..

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

[4]  Jacob Goldberger,et al.  Classification of hyperspectral remote-sensing images using discriminative linear projections , 2009 .

[5]  E. LeDrew,et al.  Remote sensing of aquatic coastal ecosystem processes , 2006 .

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

[7]  Manuela M. Veloso,et al.  Prioritized Multihypothesis Tracking by a Robot with Limited Sensing , 2009, EURASIP J. Adv. Signal Process..

[8]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Jon Atli Benediktsson,et al.  Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas , 2009, EURASIP J. Adv. Signal Process..

[10]  Liu Ying,et al.  Hyperspectral Feature Extraction using Selective PCA based on Genetic Algorithm with Subgroups , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

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

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

[13]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[14]  Pao-Ta Yu,et al.  A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[16]  Lorenzo Bruzzone,et al.  A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  Johannes R. Sveinsson,et al.  Classification and feature extraction of AVIRIS data , 1995, IEEE Trans. Geosci. Remote. Sens..