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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
  2. Altmetric Badge
    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
Attention for Chapter 10: Crop Classification and Mapping for Agricultural Land from Satellite Images
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Citations

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Chapter title
Crop Classification and Mapping for Agricultural Land from Satellite Images
Chapter number 10
Book title
Artificial Intelligence Techniques for Satellite Image Analysis
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-24178-0_10
Book ISBNs
978-3-03-024177-3, 978-3-03-024178-0
Authors

A. Kalaivani, Rashmita Khilar

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Unspecified 2 17%
Student > Bachelor 2 17%
Student > Master 1 8%
Unknown 4 33%
Readers by discipline Count As %
Engineering 3 25%
Unspecified 2 17%
Earth and Planetary Sciences 2 17%
Environmental Science 1 8%
Computer Science 1 8%
Other 0 0%
Unknown 3 25%