A classification-based linear projection of labeled hyperspectral data

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2007, Pages 3202-3205

  Weizman, Lior ^a   Goldberger, Jacob ^a  

^a BAR ILAN UNIVERSITY   (Israel)

Author keywords

Classification; Hyperspectral images; Linear projection; NCA; Remote sensing

Indexed keywords

HYPERSPECTRAL DATA CLASSIFICATION; LINEAR PROJECTION; LINEAR SUBSPACE METHOD; NEIGHBOURHOOD COMPONENT ANALYSIS (NCA);

CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; IMAGE PROCESSING; INFRARED IMAGING;

REMOTE SENSING;

EID: 77954229259     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2007.4423526     Document Type: Conference Paper

References (19)

1. Aviris nw indianas indian pines 1992 data set [online]. Available:ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C(original files)and ftp://ftp.ecn.purdue.edu/biehl/PC_MultiSpec /ThyFiles.zip (ground truth).
2. Spcim web-site. Available:http://www.specim.fi/products-aisa-eagle.html.
3. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall. Learning a mahalanobis distance from equivalence constarints. Journal of Machine Learning Research, pages 937-965, 2005.
4. L. Bruzzone and D. Fernndez-Prieto. A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens., 37(2):1179-1184, 1999.
5. G. Camps-Valls and L. Bruzzone. Kernel-based methods for hyperspectral images classification. IEEE Trans. Geosci. Remote Sens., 43(6):1351-1362, 2005.
6. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla. Composite kernels for hyperspectral image classification. Geosci. and Remote Sens. Letters, IEEE, 3:93-97, 2006.
7. C.-I. Chang. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer, 2003.
8. C.-I Chang and Q. Du. Interference and noise adjusted principal components analysis. IEEE Trans. Geosci. Remote Sensing, 37:2387-2396, 1999.
9. M. P. Derde and D. L. Massart. Evaluation of the required sample size in some supervised pattern recognition techniques. Analytica Chimica Acta, 223(1): 19-44, 1989.
10. M. M. Dundar and D. A. Landgrebe. Toward an optimal supervised classifier for the analysis of hyperspectral data. IEEE Trans. Geosci. Remote Sens., 42(1):271-277, 2004.
11. M. Murat Dundar and A. Landgrebe. A cost-effective semisupervised classifier approach with kernels. IEEE Trans. Geosci. Remote Sens., 42(8):1778-1796, 2004.
12. A. A. Green et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sensing, 26:65-74, 1998.
13. M. E. Martin et al. Determining forest species composition using high spectral resolution remote sensing data. Remote Sens. Environ., 65:249-254, 1998.
14. J. Goldberger, S. Roweis, G. Hinton, and R. Salkhutdinov. Neighbourhood components analysis. Advances in Neural Information Processing Systems (NIPS), 2004.
15. G. F. Hughes. On the mean accuracy of statistical pattern recognition. IEEE Trans. Inform. Theory, IT-14:55-63, 1968.
16. J. B. Lee, A. S.Woodyatt, and M. Berman. Enhancement of high-spectral resolution remote-sensing data by a noise-adjusted principal components transform. IEEE Trans. Geosci. Remote Sensing, 28:295-304, 1990.
17. F. Melgani and L. Bruzzone. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens., 42(8):1778-1796, 2004.
18. R. Nishii and S. Eguchi. Supervised image classification by contextual adaboost based on posteriors in neighborhoods. IEEE Trans. on Geosci. and Remote Sens., 43:2547-2554, 2005.
19. A. J. Richards and X. Jia. Remote Sensing Digital Image Analysis. Springer-Verlag, 1999.