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Mathematical Models for Remote Sensing Image Processing

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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 7: Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification
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