Finding optimal neural networks for land use classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 36, Issue 1, 1998, Pages 337-341

  Bischof, Horst ^a   Leonardis, Ales ^b  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Gaussian maximum likelihood classifier; Land use classification; Minimum description length (mdl); Multilayer perceptron; Optimizing neural networks

Indexed keywords

ALGORITHMS; LAND USE; MULTISPECTRAL SCANNERS; NEURAL NETWORKS; OPTIMIZATION; STATISTICAL METHODS;

GAUSSIAN MAXIMUM LIKELIHOOD CLASSIFIER; LAND USE CLASSIFICATION; MINIMUM DESCRIPTION LENGTH (MDL);

REMOTE SENSING;

EID: 0031673762     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/36.655348     Document Type: Article

References (16)

1. H. Bischof, W. Schneider, and A. Pinz, Multispectral classification of Landsat-images using neural networks IEEE Trans. Geosci. Remote Sensing, vol. 30, pp. 482-490, Mar. 1992.
2. H. K. Greenspan and R. Goodman, Remote sensing image analysis via a texture classification neural network in Advances in Neural Information Processing Systems 5, S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann, 1993, pp. 425-432.
3. Y. Hara, R. Atkins, S. Yueh, R. Shin, and J. Kong, Application of neural networks to radar image classification IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 100-109, Jan. 1994.
4. A. S. Solberg, A. Jain, and T. Taxt, Multisource classification of remotely sensed data: Fusion of Landsat TM and SAR images IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 768-778, Apr. 1994.
5. V. Vapnik, E. Levin, and Y. LeCun, Measuring the VC-dimension of a learning machine Neural Comput., vol. 6, no. 5, pp. 851-876, 1994.
6. Y. L. Le Cun, J. S. Denker, and S. A. Solla, Optimal brain damage in Advances in Neural Information Processing Systems 2, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1988, pp. 598-605.
7. B. Hassibi and D. Stork, Second order derivatives for network pruning: Optimal brain surgeon in NIPS 5. San Mateo, CA: Morgan Kaufmann, 1993, pp. 164-172.
8. J. Rissanen, Universal coding, information, prediction, and estimation IEEE Trans. Inform. Theory, vol. IT-30, pp. 629-636, July 1984.
9. A. Leonardis, A. Gupta, and R. Bajcsy, Segmentation of range images as the search for geometric parametric models Int. J. Comput. Vis., vol. 14, no. 3, pp. 253-277, 1995.
10. A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing. New York: Wiley, 1993.
11. F. Glover and M. Laguna, Tabu search in Modern Heuristic Techniques for Combinatorial Problems, C. R. Reeves, Ed. Oxford, U.K.: Blackwell, 1993, pp. 70-150.
12. H. Bischof, Pyramidal Neural Networks. Princeton, NJ: Lawrence Erlbaum, 1995.
13. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
14. R. Wagner, Verfolgung der Siedlungsentwicklung mit Landsat TM Bilddaten (Settlement development with Landsat TM data) Ph.D. dissertation, Inst. f. Surveying Remote Sensing, Univ. Natural Resources, Vienna, Austria, 1991.
15. G. E. Hinton, J. L. McClelland, and D. E. Rumelhart, Distributed representations in Parallel Distributed Processing, D. Rumelhart and J. McClelland, Eds. Cambridge, MA: MIT Press, 1986, vol. l, pp. 77-109.
16. A. Leonardis and H. Bischof, Complexity optimization of adaptive RBF networks in Proc. 13th Int. Conf. Pattern Recogn., IEEE Comput. Soc., 1996, vol. IV, pp. 654-658.