Artificial Neural Networks in Remote Sensing of Hydrologic Processes-Reference-Cited by-同舟云学术

Artificial Neural Networks in Remote Sensing of Hydrologic Processes

Published:2000-04 Issue:2 Volume:5 Page:138-144
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Islam Shafiqul¹²,Kothari Ravi¹²

Affiliation:

1. PhD, Cincinnati Earth Sys. Sci. Program, Dept. of Civ. and Envir. Engrg., Univ. of Cincinnati, Cincinnati, OH 45221. E-mail: shafiqul. islam@uc.edu.

2. Dept. of Electr. and Comp. Engrg. and Comp. Sci., Univ. of Cincinnati, Cincinnati, OH.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Water Science and Technology,Civil and Structural Engineering,Environmental Chemistry

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%291084-0699%282000%295%3A2%28138%29

Reference53 articles.

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3. Augusteijn M. F. Clemens L. E. and Shaw K. A. ( 1995). “Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier.” IEEE Trans. on Geosci. and Remote Sensing Piscataway N.J. 33(3) 616–626.

4. Barron A. R. ( 1993). “Universal approximation bounds for superposition of a sigmoidal function.” IEEE Trans. on Information Theory Piscataway N.J. 39(3) 930–945.

5. Baum E. and Haussler D. (1989). “What sized net gives valid generalization.” Neural Computation 1(1) 151–160.

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