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Solving inversion problems with neural networksA class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.
Document ID
19920039072
Document Type
Conference Paper
Authors
Kamgar-Parsi, Behzad (Maryland, University College Park, United States)
Gualtieri, J. A. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1990
Subject Category
CYBERNETICS
Meeting Information
IJCNN - International Joint Conference on Neural Networks(San Diego, CA)
Distribution Limits
Public
Copyright
Other