Improving the performance of classifiers in high-dimensional remote sensing applications: An adaptive resampling strategy for error-prone exemplars (ARESEPE)

IEEE Transactions on Geoscience and Remote Sensing

Volumn 41, Issue 9 PART I, 2003, Pages 2101-2112

  Bachmann, Charles M ^a  

^a NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Active learning; Active sampling; Barrier islands; Hyperspectral; Land cover classification; Virginia coast reserve

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; IMAGE ANALYSIS; ONLINE SYSTEMS; OPTIMIZATION; STATISTICAL MECHANICS;

BARRIER ISLANDS; ERROR PRONE EXEMPLARS; HYPERSPECTRAL; LAND COVER CLASSIFIERS;

REMOTE SENSING;

EID: 0141905876     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2003.817207     Document Type: Article

References (15)

1. C. M. Bachmann and M. H. Bettenhausen, "PURSUIT: An automatic classification tool for remote sensing data," in Proc. IGARSS, Toronto, ON, Canada, 2002.
2. C. M. Bachmann, T. F. Donato, G. M. Lamela, W. J. Rhea, M. H. Betenhausen, R. A. Fusina, K. DuBois, J. H. Porter, and B. R. Truitt, "Automatic classification of land-cover on Smith Island, VA using HYMAP imagery," IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 2313-2330, Oct. 2002.
3. C. M. Bachmann, M. H. Bettenhausen, R. A. Fusina, T. F. Donato, A. L. Russ, J. Burke, G. M. Lamela, W. J. Rhea, B. R. Truitt, and J. H. Porter, "A credit assignment approach to fusing classifiers of multi-season hyperspectral imagery," IEEE Trans. Geosci. Remote Sensing, to be published.
4. J. Boardman, "Post-ATREM polishing of AVIRIS apparent reflectance data using EFFORT: A lesson in accuracy versus precision," in Summaries of the 7th Ann. JPL Airborne Geoscience Workshop. Pasadena, CA, 1998.
5. ENVI 3.4 User's Guide, Research Systems, Inc., Boulder, CO, 2000.
6. 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, 1999.
7. K. Fukumizu, "Statistical active learning in multilayer perceptrons," IEEE Trans. Neural Networks, vol. 11, pp. 17-26, Jan. 2000.
8. B. Gao and C. O. Davis, "Development of a line-by-line atmosphere removal algorithm for airborne and spaceborne imaging spectrometers," Proc. SPIE, vol. 3118, pp. 132-141, 1997.
9. J.-N. Hwang, J. J. Choi, S. Oh, and R. J. M. II, "Query-based learning applied to partially trained multilayer perceptrons," IEEE Trans. Neural Networks, Vol. 2, pp. 131-136, Jan. 1991.
10. J. H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Processing, vol. 40, pp. 3043-3054, Dec. 1992.
11. T. Kohonen, Self Organizing Maps, 2nd ed. Berlin, Germany: Springer-Verlag, 1997.
12. J.-M. Park and Y. H. Hu, "Adaptive on-line learning of optimal decision boundary using active sampling," in Proc. 1996 Workshop Neural Networks for Signal Processing VI, S. Usui, Y. Tohkura, S. Katagiri, and E. Wilson, Eds., Kyoto, Japan, 1996, pp. 253-262.
13. M. D. Richard and R. P. Lippman, "Neural network classifiers estimate Bayesian a posteriori probabilities," Neural Comput., 3, pp. 461-483, 1991.
14. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing, Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: MIT Press, 1986. vol. 1 Foundations, pp. 318-362.
15. K. Yamauchi, N. Yamaguchi, and N. Ishii, "An incremental learning method with relearning of recalled interfered patterns," in Proc. 1996 Workshop Neural Networks for Signal Processing VI, S. Usui, Y. Tohkura, S. Katagiri, and E. Wilson, Eds., Kyoto, Japan, 1996, 243-252.