Neural Networks in Atmospheric Remote Sensing
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Neural Networks in Atmospheric Remote Sensing

Neural Networks in Atmospheric Remote Sensing


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About the Book

In the electrical engineering field, a neural network refers to interconnecting artificial neurons that mimic the properties of biological neurons to perform sophisticated, intelligent tasks. This authoritative reference offers a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. Professionals find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. Engineers discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

Table of Contents:
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.

About the Author :
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. 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. David H. Staelin is a professor of electrical engineering in the Research Laboratory of Electronics at MIT.


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Product Details
  • ISBN-13: 9781596933729
  • Publisher: Artech House Publishers
  • Publisher Imprint: Artech House Publishers
  • Edition: Unabridged edition
  • No of Pages: 234
  • Returnable: Y
  • ISBN-10: 1596933720
  • Publisher Date: 30 Jun 2009
  • Binding: Hardback
  • Language: English
  • Returnable: Y


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