Discriminant Manifold Learning Approach for Hyperspectral Image Dimension Reduction

DU Bo; ZHANG Le-fei; ZHANG Liang-pei; HU Wen-bin

doi:10.3788/gzxb20134203.0320

[1] CHANG C I. Hyperspectral data exploitation: theory and applications［M］. New Jersey: Wiley-Interscience, 2007.

[2] GOETZ A F H, VANE G, SOLOMON J E, et al. Imaging spectrometry for earth remote sensing［J］. Science, 1985, 228(4704): 1147-1153.

[3] KESHAVA N, MUSTARD J F. Spectral unmixing［J］. IEEE Signal Processing Magazine, 2002, 19(1): 44-57.

[4] HUGHES G F. On the mean accuracy of statistical pattern recognizers［J］. IEEE Transactions on Information Theory, 1968, 14(1): 55-63.

[5] BACHMANN C M, AINSWORTH T L, FUSINA R A. Exploiting manifold geometry in hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 441-454.

[6] CHANG C I, WANG S. Constrained band selection for hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1575-1585.

[7] HARSANYI J C, CHANG C I. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach［J］. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 779-785.

[8] FARRELL J M D, MERSEREAU R M. On the impact of pca dimension reduction for hyperspectral detection of difficult targets［J］. IEEE Geoscience and Remote Sensing Letters, 2005, 2(2): 192-195.

[9] FISHER R A. The use of multiple measurements in taxonomic problems［J］. Annals of Human Genetics, 1936, 7(2): 179-188.

[10] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding［J］. Science, 2000, 290(22): 2323-2326.

[11] TENENBAUM J B, SILVA V D, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction［J］. Science, 2000, 290(22): 2319-2323.

[12] CAI D, HE X, HAN J, et al. Orthogonal laplacianfaces for face recognition［J］. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614.

[13] CRAWFORD M M, MA L, KIM W. Exploring nonlinear manifold learning for classification of hyperspectral data［M］. // PRASAD S, et al. Optical remote sensing. Heidelberg: Springer, 2011.

[14] DONOHO D L, GRIMES C. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data［J］. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(10): 5591-5596.

[15] YAN S, XU D, ZHANG B, et al. Graph embedding and extensions: a general framework for dimensionality reduction［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.

[16] ZHANG T, TAO D, LI X, et al. Patch alignment for dimensionality reduction［J］. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1299-1313.

[17] HE X, NIYOGI P. Locality preserving projections［C］. in Advances in Neural Information Processing Systems, 2004, 153-160.

[18] MA Y, NIYOGI P, SAPIRO G, et al. Dimensionality reduction via subspace and submanifold learning［J］. IEEE Signal Processing Magazine, 2011, 28(2): 14-15.

[19] CHUNG F R K. Spectral graph theory［M］. Rhode Island: American Mathematical Society, 1997.

[20] LANDGREBE D A. Signal theory methods in multispectral remote sensing［M］. New Jersey: Wiley, 2003.

[21] KUO B, LANDGREBE D A. Nonparametric weighted feature extraction for classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(5): 1096-1105.

[22] DU B, ZHANG L, ZHANG L, et al. A discriminative manifold learning based dimension reduction method for hyperspectral classification［J］. International Journal of Fuzzy Systems, 2012, 14(2): 272-277.

CLP Journals