Spectral mixture analysis of hyperspectral scenes using intelligently selected training samples

IEEE Geoscience and Remote Sensing Letters

Volumn 7, Issue 2, 2010, Pages 371-375

  Plaza, Javier ^a   Plaza, Antonio ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Hyperspectral imaging; Intelligent training; Neural networks; Spectral mixture analysis

Indexed keywords

AIRBORNE DATA; APRIORI; ARTIFICIAL FOREST; ARTIFICIAL NEURAL NETWORK; CANOPY MODEL; DIGITAL AIRBORNE IMAGING SPECTROMETERS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGING; IMAGING SPECTROMETERS; INPUT DATAS; LINEAR MIXTURE MODELS; MULTI LAYER PERCEPTRON; NEURAL ARCHITECTURES; OBJECT GEOMETRIES; PRIOR INFORMATION; RADIATION INTERCEPTION; REFLECTIVE OPTICS; SPATIAL RESOLUTION; SPECTRAL MIXTURE ANALYSIS; TRAINING SAMPLE;

LIGHT; MIXTURES; OPTOELECTRONIC DEVICES; SPECTROMETERS; SPECTROMETRY;

NEURAL NETWORKS;

EID: 77951207013     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2009.2036139     Document Type: Article

References (19)

1. J. B. Adams,M. O. Smith, and P. E. Johnson, "Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site," J. Geophys. Res., vol. 91, no. B8, pp. 8098-8112, Jul. 1986.
2. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol.42, no.3, pp. 650-663, Mar. 2004.
3. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol.19, no.1, pp. 44-57, Jan. 2002.
4. D. Heinz and C.-I. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol.39, no.3, pp. 529-545, Mar. 2001.
5. J. Plaza, A. Plaza, P. Martinez, and R. Perez, "Joint linear/nonlinear spectral unmixing of hyperspectral image data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2007, vol.1, pp. 4037-4040.
6. K. J. Guilfoyle, M. L. Althouse, and C.-I. Chang, "A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks," IEEE Trans. Geosci. Remote Sens., vol.39, no.10, pp. 2314-2318, Oct. 2001.
7. W. Liu and E. Y. Wu, "Comparison of non-linear mixture models," Remote Sens. Environ., vol.18, pp. 1976-2003, 2004.
8. C. C. Borel and S. A. W. Gerslt, "Nonlinear spectral mixing models for vegetative and soil surfaces," Remote Sens. Environ., vol.47, no.3, pp. 403-416, Mar. 1994.
9. N. S. Goel and R. L. Thompson, "A snapshot of canopy reflectance models and a universal model for the radiation regime," Remote Sens. Rev., vol.18, no.2-4, pp. 197-225, 2000.
10. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford Univ. Press, 1995.
11. A. Baraldi, E. Binaghi, P. Blonda, P. A. Brivio, and P. Rampini, "Comparison of the multilayer perceptron with neuro-fuzzy techniques in the estimation of cover class mixture in remotely sensed data," IEEE Trans. Geosci. Remote Sens., vol.39, no.5, pp. 994-1005, May 2001.
12. M. Chi and L. Bruzzone, "A semilabeled-sample-driven bagging technique for ill-posed classification problems," IEEE Geosci. Remote Sens. Lett., vol.2, no.1, pp. 69-73, Jan. 2005.
13. B. Hu, J. R. Miller, J. Wang, and N. S. Goel, "Investigation of linear spectral mixtures of the reflectance spectra using laboratory simulated forest scenes," in Proc. IEEE Int. Geosci. Remote Sens. Symp., vol.3, 2002, pp. 1535-1537.
14. C.-I. Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol.42, no.3, pp. 608-619, Mar. 2004.
15. A. Vehtari and J. Lampinen, "Bayesian MLP neural networks for image analysis," Pattern Recognit. Lett., vol. 21, no. 13/14, pp. 1183-1191, Dec. 2000.
16. X. Zhuang, B. A. Engel, D. F. Lozano, R. B. Fernández, and C. J. Johannsen, "Optimization of training data required for neuroclassification, " Int. J. Remote Sens., vol.15, no.16, pp. 3271-3277, Nov. 1999.
17. G. M. Foody, "The significance of border training patterns in classification by a feedforward neural network using back propagation learning," Int. J. Remote Sens., vol.20, no.18, pp. 3549-3562, Dec. 1999.
18. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," Proc. SPIE, vol.3753, pp. 266-277, Oct. 1999.
19. J. Plaza, A. Plaza, P. Martinez, and R. Perez, "Nonlinear mixture models for analyzing laboratory simulated-forest hyperspectral data," Proc. SPIE, vol.5508, pp. 660-670, 2003.