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Neural Networks in Atmospheric Remote Sensing
William J. Blackwell, MIT Lincoln Laboratory and Frederick W. Chen, Signal Systems Corporation
ISBN 978-1-59693-372-9
Copyright 2009
Pages: 234
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This authoritative reference offers you a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. You find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. The book provides clear explanations of the mathematical and physical foundations of remote sensing systems, including radiative transfer and propagation theory, sensor technologies, and inversion and estimation approaches. You discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

Software Included

CD-ROM Included! Contains MATLAB software codes to accompany many of the examples presented in the book that can be used as building blocks for larger and more complex problems.

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William J. Blackwell is on the technical staff at the MIT Lincoln Laboratory and is currently a science team member involved with atmospheric sounding systems aboard NPOESS and NASA EOS/NPP Missions. He received an S.M. and Sc.D. in electrical engineering from the Massachusetts Institute of Technology.

Frederick W. Chen was most recently a technical staff member at the MIT Lincoln Laboratory, where he worked on problems in satellite-based atmospheric remote sensing using microwave and infrared data. He holds an S.B., M.Eng., and Ph.D. in electrical engineering from the Massachusetts Institute of Technology.

Introduction. Physical Background of Atmospheric Remote Sensing. An Overview of Inversion Problems in Atmospheric Remote Sensing. Signal Processing/Data Representation. Introduction of Neural Networks/Multilayer Perceptrons. Neural Networks Model Selection, Initialization, and Training. Preprocessing and Postprocessing of Atmospheric Data. Evaluation and Validation of Neural Network Performance. Retrieval of Precipitation from Passive Spaceborne Microwave Observations. Neural Network Retrieval of Atmospheric Temperature and Moisture Profiles in Cloudy Conditions from Microwave and Hyperspectral Infrared Observations.

Click here to view complete Table of Contents in PDF format.

"Blackwell (senior technical staff at the Massachusetts Institute of Technology's Lincoln Laboratory) and Chen (senior engineer at Signal Systems Corporation) present an applications-oriented treatment of neural network methodologies for use in atmospheric remote sensing. Their focus is on the retrieval of atmospheric parameters, such as the Earth's temperature, water vapor profiles, and precipitation rate, but the methodologies can also be applied to other problems where function approximation is required. They begin with simple, theoretical examples demonstrating how performance is affected by basic neural network attributes such as model selection, initialization, and training methodology and then build on those to describe applications common in atmospheric remote sensing. The examples are often accompanied by MATLAB software codes, available on the accompanying CD-ROM, which can be used for larger and more complex problems".

Book News (SciTech), September 2009