Lagrange constrained neural network-based approach to hyperspectral remote sensing image classification

Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03

Volumn 1, Issue , 2003, Pages 270-274

  Du, Qian ^a   Szu, Harold ^b   Buss, Jim ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b OFFICE OF NAVAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTRAST FUNCTIONS; DATA ASSESSMENT; ENDMEMBERS; FRACTION IMAGES; HELMHOLTZ FREE ENERGY; HIGHLY-CORRELATED; HYPERSPECTRAL; HYPERSPECTRAL REMOTE SENSING IMAGE; IMAGE SCENE; LAGRANGE; LINEAR ESTIMATION; MAXIMUM VALUES; NETWORK-BASED APPROACH; NON-NEGATIVITY; POST PROCESSING; PRIOR INFORMATION; RADIATION FIELD; REMOTELY SENSED IMAGES; THERMODYNAMICS EQUILIBRIUM; TWO STAGE;

FREE ENERGY; IMAGE RECONSTRUCTION; LAGRANGE MULTIPLIERS; MAXIMUM LIKELIHOOD ESTIMATION; REMOTE SENSING; SIGNAL PROCESSING; THERMODYNAMICS;

NEURAL NETWORKS;

EID: 54749109444     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNNSP.2003.1279263     Document Type: Conference Paper

References (13)

1. R. B. Singer and T. B. McCord, "Mars: large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance," Proc. 10th Lunar Planet. Sci. Conf., pp. 1835-1848, 1979.
2. J. B. Adams and M. O. Smith, "Spectral mixture modeling: a new analysis of rock and soil types at the Viking lander 1 suite," J. Geophysical Res., vol. 91, no. B8, pp. 8098-8112, July 10, 1986.
3. J. J. Settle and N. A. Drake, "Linear mixing and estimation of ground cover proportions," International Journal of Remote Sensing, vol. 14, pp. 11594177, 1993.
4. J. B. Adams, M. O. Smith and A. R. Gillespie, Image spectroscopy: interpretation based on spectral mixture analysis. Remote Geochemical Analysis: Elemental and Mineralogical Composition, edited by CM. Pieters and P.A. Englert, pp. 145-166 (Mass: Cambridge University Press), 1993.
5. L. L. Scharf, Statistical Signal Processing, Addison-Wesley, 1991.
6. D. Heínz and C.-I Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 3, pp. 529-545, 2001.
7. H. H. Szu and H. Ren, "Unified Lagrangian Neural Network Method for Subpixel Classification in Hyperspectral Imagery" Proceedings of SPIE, vol. 4391, pp. 314-329. 2001.
8. J. Harsanyi, W. Farrand and C.-I Chang, "Determining the number and identity of spectral endmembers: an integrated approach using Neyman-Pearson eigen-thresholding and iterative constrained RMS error minimization," Proc. Ninth Thematic Conference on Geologic Remote Sensing, February, 1993.
9. T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd. ed., John Wiley & Sons, New York, 1984.
10. H. V. Poor, An Introduction to Detection and Estimation Theory, 2n ed., Springer-Verlag, New York, 1994.
11. C.-I Chang and Q. Du, "A noise subspace projection approach to determination of intrinsic dimensionality for hyperspectral imagery," EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, SPIE vol. 3871, Florence, Italy, pp. 34-44, September 20-24, 1999.
12. C.-I Chang, Q. Du, T.-L. Sun and M. L. G. Althouse, "A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification," IEEE Transactions On Geoscience and Remote Sensing, vol. 37, no. 5, pp. 2631-2641, 1999.
13. C. Thomas, Elements of Information Theory, Wiley-Interscience, 1991.