Import vector machines for quantitative analysis of hyperspectral data

IEEE Geoscience and Remote Sensing Letters

Volumn 11, Issue 2, 2014, Pages 449-453

  Suess, Stefan ^a   Van Der Linden, Sebastian ^a   Leitão, Pedro J ^a   Okujeni, Akpona ^a   Waske, Björn ^b   Hostert, Patrick ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Hyperspectral; Import Vector Machines (IVM); Parameter selection; Quantitative mapping; Subpixel analysis

Indexed keywords

ELECTRICAL ENGINEERING; GEOTECHNICAL ENGINEERING;

HYPERSPECTRAL; IMPORT VECTOR MACHINES; PARAMETER SELECTION; QUANTITATIVE MAPPING; SUB-PIXEL ANALYSIS;

RANDOM ERRORS;

EID: 84896396112     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2013.2265102     Document Type: Article

References (21)

1. T. Stuffler, C. Kaufmann, S. Hofer, K. P. Forster, G. Schreier, A. Mueller, A. Eckardt, H. Bach, B. Penne, U. Benz, and R. Haydn, "The EnMAP hyperspectral imager - An advanced optical payload for future applications in Earth observation programmes," Acta Astron., vol. 61, no. 1-6, pp. 115-120, Jun./Aug. 2007.
2. D. A. Roberts, D. A. Quattrochi, G. C. Hulley, S. J. Hook, and R. O. Green, "Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) decadal survey mission," Remote Sens. Environ., vol. 117, pp. 83-101, Feb. 2012.
3. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 354-379, Apr. 2012.
4. K. H. Liu, E. L. Wong, E. Y. Du, C. C. C. Chen, and C. I. Chang, "Kernel-based linear spectral mixture analysis," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 1, pp. 129-133, Jan. 2012.
5. J. Plaza and A. Plaza, "Spectral mixture analysis of hyperspectral scenes using intelligently selected training samples," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 2, pp. 371-375, Apr. 2010.
6. D. A. Roberts, M. Gardner, R. Church, S. Ustin, G. Scheer, and R. O. Green, "Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models," Remote Sens. Environ., vol. 65, no. 3, pp. 267-279, Sep. 1998. (Pubitemid 29360703)
7. Y. Z. Pan, T. G. Hu, X. F. Zhu, J. S. Zhang, and X. D. Wang, "Mapping cropland distributions using a hard and soft classification model," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4301-4312, Nov. 2012.
8. H. Li, Y. P. Wang, Y. Li, and X. F. Wang, "Pixel-unmixing moderatere-solution remote sensing imagery using pairwise coupling support vector machines: A case study," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4298-4307, Nov. 2011.
9. G. M. Foody, "Fuzzy modeling of vegetation from remotely sensed imagery," Ecol. Model., vol. 85, no. 1, pp. 3-12, Feb. 1996.
10. A. Okujeni, S. van der Linden, L. Tits, B. Somers, and P. Hostert, "Support vector regression and synthetically mixed training data for quantifying urban landcover," Remote Sens. Environ., to be published.
11. J. T. Walton, "Subpixel urban land cover estimation: Comparing cubist, random forests, and support vector regression," Photogramm. Eng. Remote Sens., vol. 74, no. 10, pp. 1213-1222, Oct. 2008.
12. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005. (Pubitemid 40811944)
13. B. Waske, S. van der Linden, J. A. Benediktsson, A. Rabe, and P. Hostert, "Sensitivity of support vector machines to random feature selection in classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 7, pp. 2880-2889, Jul. 2010.
14. J. Zhu and T. Hastie, "Kernel logistic regression and the import vector machine," J. Comput. Graph. Stat., vol. 14, no. 1, pp. 185-205, Mar. 2005. (Pubitemid 40447832)
15. A. C. Braun, U.Weidner, and S. Hinz, "Classification in high-dimensional feature spaces-Assessment using SVM, IVM and RVM with focus on simulated EnMAP data," IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 436-443, Apr. 2012.
16. R. Roscher, B. Waske, and W. Foerstner, "Incremental import vector machines for classifying hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 9, pp. 3463-3473, Sep. 2012.
17. M. Brown, H. G. Lewis, and S. R. Gunn, "Linear spectral mixture models and support vector machines for remote sensing," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2346-2360, Sep. 2000.
18. Q. Cheng, P. K. Varshney, and M. K. Arora, "Logistic regression for feature selection and soft classification of remote sensing data," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 4, pp. 491-494, Oct. 2006. (Pubitemid 44679657)
19. 2 VM: Incremental import vector machines," Image Vis. Comput., vol. 30, no. 4/5, pp. 263-278, May 2012.
20. B. Somers, M. Zortea, A. Plaza, and G. P. Asner, "Automated extraction of image-based endmember bundles for improved spectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 396-408, Apr. 2012.
21. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.