Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 9088, Issue , 2014, Pages

  Cahill, Nathan D ^a   Czaja, Wojciech ^b   Messinger, David W ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

^b Bldg 142   (United States)

Author keywords

Dimensionality reduction; Laplacian eigenmaps; Schroedinger eigenmaps; Spatial spectral fusion

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; REMOTE SENSING;

DIMENSIONALITY REDUCTION; GRAPH-BASED TECHNIQUES; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL IMAGE DATAS; LAPLACIAN EIGENMAPS; MACHINE LEARNING TECHNIQUES; SCHROEDINGER EIGENMAPS; SPECTRAL INFORMATION;

SPECTROSCOPY;

EID: 84906254465     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2050651     Document Type: Conference Paper

References (15)

1. Schott, J. R., [Remote Sensing: The Image Chain Approach], Oxford University Press, 2nd ed. (2007).
2. Prasad, S. and Bruce, L., "Limitations of principal components analysis for hyperspectral target recognition," Geoscience and Remote Sensing Letters, IEEE 5, 625-629 (Oct 2008).
3. Kim, D. and Finkel, L., "Hyperspectral image processing using locally linear embedding," in [Neural Engineering, 2003. Conference Proceedings. First International IEEE EMBS Conference on], 316-319 (March 2003).
4. Bachmann, C., Ainsworth, T., and Fusina, R., "Exploiting manifold geometry in hyperspectral imagery," Geoscience and Remote Sensing, IEEE Transactions on 43, 441-454 (March 2005). (Pubitemid 40320267)
5. Fauvel, M., Chanussot, J., and Benediktsson, J., "Kernel principal component analysis for the classification of hyperspectral remote sensing data of urban areas," EURASIP Journal on Advances in Signal Processing 2009(783194), 1-14 (2009).
6. Halevy, A., Extensions of Laplacian Eigenmaps for Manifold Learning, PhD thesis, University of Maryland, College Park (2011).
7. Hou, B., Zhang, X., Ye, Q., and Zheng, Y., "A novel method for hyperspectral image classification based on Laplacian eigenmap pixels distribution-flow," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 6(3), 1602-1618 (2013).
8. Benedetto, J., Czaja, W., Dobrosotskaya, J., Doster, T., Duke, K., and Gillis, D., "Integration of heterogeneous data for classification in hyperspectral satellite imagery," in [Proc. of SPIE Vol. 8390], 839027-1-839027-12 (June 2012).
9. Gillis, D. B. and Bowles, J. H., "Hyperspectral image segmentation using spatial-spectral graphs," Proc. SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII 8390, 83901Q-1-83901Q-11 (2012).
10. Czaja, W. and Ehler, M., "Schroedinger eigenmaps for the analysis of biomedical data," IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1274-1280 (May 2013).
11. Belkin, M. and Niyogi, P., "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Computation 15, 1373-1396 (June 2003). (Pubitemid 37049796)
12. Shi, J. and Malik, J., "Normalized cuts and image segmentation," in [Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on], 731-737 (1997).
13. Shi, J. and Malik, J., "Normalized cuts and image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(8), 888-905 (2000).
14. Benedetto, J., Czaja, W., Dobrosotskaya, J., Doster, T., Duke, K., and Gillis, D., "Semi-supervised learning of heterogeneous data in remote sensing imagery," in [Proc. of SPIE Vol. 8401], 840104-1-840104-12 (June 2012).
15. Senseman, G. M., Bagley, C. F., and Tweddale, S. A., "Accuracy assessment of the discrete classification of remotely-sensed digital data for landcover mapping," in [USACERL Technical Report EN-95/04], 1-27 (April 1995).