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Mathematical Models for Remote Sensing Image Processing

Overview of attention for book
Cover of 'Mathematical Models for Remote Sensing Image Processing'

Table of Contents

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    Book Overview
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    Chapter 1 Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction
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    Chapter 2 Models for Hyperspectral Image Analysis: From Unmixing to Object-Based Classification
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    Chapter 3 Very High Spatial Resolution Optical Imagery: Tree-Based Methods and Multi-temporal Models for Mining and Analysis
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    Chapter 4 Very-High-Resolution and Interferometric SAR: Markovian and Patch-Based Non-local Mathematical Models
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    Chapter 5 Polarimetric SAR Modelling: Mellin Kind Statistics and Time-Frequency Analysis
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    Chapter 6 Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion
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    Chapter 7 Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification
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    Chapter 8 Change Detection in Multitemporal Images Through Single- and Multi-scale Approaches
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    Chapter 9 Satellite Image Time Series: Mathematical Models for Data Mining and Missing Data Restoration
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    Chapter 10 Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
Attention for Chapter 10: Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
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Citations

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Chapter title
Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval
Chapter number 10
Book title
Mathematical Models for Remote Sensing Image Processing
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-66330-2_10
Book ISBNs
978-3-31-966328-9, 978-3-31-966330-2
Authors

Devis Tuia, Michele Volpi, Jochem Verrelst, Gustau Camps-Valls

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Researcher 2 25%
Student > Doctoral Student 1 13%
Unknown 3 38%
Readers by discipline Count As %
Earth and Planetary Sciences 3 38%
Agricultural and Biological Sciences 2 25%
Unknown 3 38%

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