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Application of Artificial Neural Computation in Topex Waveform Data: A Case Study on Water Ratio Regression

Application of Artificial Neural Computation in Topex Waveform Data: A Case Study on Water Ratio Regression

Bo Zhang, Franklin W. Schwartz, Daoqin Tong
Copyright: © 2009 |Volume: 1 |Issue: 3 |Pages: 11
ISSN: 1942-9045|EISSN: 1942-9037|ISSN: 1942-9045|EISBN13: 9781616921163|EISSN: 1942-9037|DOI: 10.4018/jssci.2009070106
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MLA

Zhang, Bo, et al. "Application of Artificial Neural Computation in Topex Waveform Data: A Case Study on Water Ratio Regression." IJSSCI vol.1, no.3 2009: pp.81-91. http://doi.org/10.4018/jssci.2009070106

APA

Zhang, B., Schwartz, F. W., & Tong, D. (2009). Application of Artificial Neural Computation in Topex Waveform Data: A Case Study on Water Ratio Regression. International Journal of Software Science and Computational Intelligence (IJSSCI), 1(3), 81-91. http://doi.org/10.4018/jssci.2009070106

Chicago

Zhang, Bo, Franklin W. Schwartz, and Daoqin Tong. "Application of Artificial Neural Computation in Topex Waveform Data: A Case Study on Water Ratio Regression," International Journal of Software Science and Computational Intelligence (IJSSCI) 1, no.3: 81-91. http://doi.org/10.4018/jssci.2009070106

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Abstract

Using the TOPEX radar altimeter for land cover studies has been of great interest due to the TOPEX near global coverage and its consistent availability of waveform data for about one and a half decades from 1992 to 2005. However, the complexity of the TOPEX Sensor Data Records (SDRs) makes the recognition of the radar echoes particularly difficult. In this article, artificial neural computation as one of the most powerful algorithms in pattern recognition is investigated for water ratio assessment over Lake of the Woods area using TOPEX reflected radar signals. Results demonstrate that neural networks have the capability in identifying water proportion from the TOPEX radar information, controlling the predicted errors in a reasonable range.

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