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Artificial Intelligence Techniques for Satellite Image Analysis

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Cover of 'Artificial Intelligence Techniques for Satellite Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Heightening Satellite Image Display via Mobile Augmented Reality – A Cutting-Edge Planning Model
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    Chapter 2 Multithreading Approach for Clustering of Multiplane Satellite Images
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    Chapter 3 Classification of Field-Level Crop Types with a Time Series Satellite Data Using Deep Neural Network
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    Chapter 4 Detection of Ship from Satellite Images Using Deep Convolutional Neural Networks with Improved Median Filter
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    Chapter 5 Artificial Bee Colony-Optimized Contrast Enhancement for Satellite Image Fusion
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    Chapter 6 Effective Transform Domain Denoising of Oceanographic SAR Images for Improved Target Characterization
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    Chapter 7 Fused Segmentation Algorithm for the Detection of Nutrient Deficiency in Crops Using SAR Images
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    Chapter 8 Detection of Natural Features and Objects in Satellite Images by Semantic Segmentation Using Neural Networks
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    Chapter 9 Change Detection of Tropical Mangrove Ecosystem with Subpixel Classification of Time Series Hyperspectral Imagery
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    Chapter 10 Crop Classification and Mapping for Agricultural Land from Satellite Images
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    Chapter 11 Next-Generation Artificial Intelligence Techniques for Satellite Data Processing
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    Chapter 12 A Wavelet Transform Applied Spectral Index for Effective Water Body Extraction from Moderate-Resolution Satellite Images
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Citations

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26 Mendeley
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Title
Artificial Intelligence Techniques for Satellite Image Analysis
Published by
Springer International Publishing, November 2019
DOI 10.1007/978-3-030-24178-0
ISBNs
978-3-03-024177-3, 978-3-03-024178-0
Editors

Hemanth, D. Jude

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Student > Master 5 19%
Professor > Associate Professor 2 8%
Professor 1 4%
Lecturer 1 4%
Other 2 8%
Unknown 9 35%
Readers by discipline Count As %
Earth and Planetary Sciences 4 15%
Engineering 3 12%
Computer Science 3 12%
Agricultural and Biological Sciences 2 8%
Psychology 2 8%
Other 1 4%
Unknown 11 42%