NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Inversion of surface parameters using fast learning neural networksA neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.
Document ID
19930063801
Document Type
Conference Paper
Authors
Dawson, M. S. (NASA Headquarters Washington, DC United States)
Olvera, J. (NASA Headquarters Washington, DC United States)
Fung, A. K. (NASA Headquarters Washington, DC United States)
Manry, M. T. (Texas Univ. Arlington, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 2 (A93-47551 20-43)
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Funding Number(s)
CONTRACT_GRANT: NGT-50585
CONTRACT_GRANT: NAGW-2344
Distribution Limits
Public
Copyright
Other

Related Records

IDRelationTitle19930063554Analytic PrimaryIGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vols. 1 & 2