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Computational Intelligence for Remote Sensing

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Cover of 'Computational Intelligence for Remote Sensing'

Table of Contents

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    Book Overview
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    Chapter 1 Optical Configurations for Imaging Spectrometers
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    Chapter 2 Remote Sensing Data Compression
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    Chapter 3 A Multiobjective Evolutionary Algorithm for Hyperspectral Image Watermarking
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    Chapter 4 Architecture and Services for Computational Intelligence in Remote Sensing
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    Chapter 5 On Content-Based Image Retrieval Systems for Hyperspectral Remote Sensing Images
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    Chapter 6 An Analytical Approach to the Optimal Deployment of Wireless Sensor Networks
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    Chapter 7 Parallel Spatial-Spectral Processing of Hyperspectral Images
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    Chapter 8 Parallel Classification of Hyperspectral Images Using Neural Networks
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    Chapter 9 Positioning Weather Systems from Remote Sensing Data Using Genetic Algorithms
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    Chapter 10 A Computation Reduced Technique to Primitive Feature Extraction for Image Information Mining Via the Use of Wavelets
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    Chapter 11 Neural Networks for Land Cover Applications
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    Chapter 12 Computational Intelligence for Remote Sensing
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    Chapter 13 Automatic Preprocessing and Classification System for High Resolution Ultra and Hyperspectral Images
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    Chapter 14 Using Gaussian Synapse ANNs for Hyperspectral Image Segmentation and Endmember Extraction
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    Chapter 15 Unsupervised Change Detection from Multichannel SAR Data by Markov Random Fields
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Title
Computational Intelligence for Remote Sensing
Published by
Springer Berlin Heidelberg, September 2008
DOI 10.1007/978-3-540-79353-3
ISBNs
978-3-54-079352-6, 978-3-54-079353-3
Editors

Graña, Manuel, Duro, Richard J.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Greece 3 6%
India 2 4%
Norway 1 2%
Brazil 1 2%
Egypt 1 2%
Portugal 1 2%
China 1 2%
Poland 1 2%
Unknown 39 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 24%
Researcher 10 20%
Student > Master 8 16%
Professor 5 10%
Student > Doctoral Student 4 8%
Other 10 20%
Unknown 1 2%
Readers by discipline Count As %
Earth and Planetary Sciences 13 26%
Computer Science 8 16%
Environmental Science 8 16%
Agricultural and Biological Sciences 7 14%
Engineering 5 10%
Other 5 10%
Unknown 4 8%