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Neural Networks in Atmospheric Remote Sensing
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Neural Networks in Atmospheric Remote Sensing (Hardback)

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Synopsis

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.

Computing & ITComputer scienceArtificial intelligenceNeural networks & fuzzy systemsScience & MathematicsGeographyGIS Geographical information systems & remote sensingTechnicalEnergy technology & engineeringElectrical engineering Publisher: Artech House Publishers Publication Date: 30/04/2009 ISBN-13: 9781596933729  Details: Type: Hardback Format: Books
Availability: To Order. Estimated despatch in 1-3 weeks.  

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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