Multi-Sensor and Multi- Temporal Remote Sensing. Specifc Single Class Mapping

✍ Scribed by Anil Kumar, Priyadarshi Upadhyay, Uttara Singh

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 177
• Category: Library

✦ Table of Contents

Cover
Half Title
Title
Copyright
Dedication
Contents
Foreword
Preface
Our Gratitude with three Rs
Author Biographies
List of Abbreviations
Chapter 1 Remote-Sensing Images
1.1 Introduction
1.2 Introduction to Multispectral Remote-Sensing
1.3 Introduction to Hyperspectral Remote-Sensing
1.3.1 Hyperspectral Data Pre-processing
1.3.2 Endmember Extraction
1.4 Introduction to SAR Remote-Sensing
1.5 Dimensionality Reduction
1.6 Summary
Bibliography
Chapter 2 Evolution of Pixel-Based Spectral Indices
2.1 Introduction
2.2 Spatial Information
2.3 Spectral Indices
2.4 Texture-Based Spatial Indices
2.5 Summary
Bibliography
Chapter 3 Multi-Sensor, Multi-Temporal Remote-Sensing
3.1 Introduction
3.2 Temporal Vegetation Indices
3.3 Specific Single Class Mapping
3.4 Indices for Temporal Data
3.5 Temporal Data With Multi-Sensor Concept
3.6 Summary
Bibliography
Chapter 4 Training Approaches—Role of Training Data
4.1 Introduction
4.2 Handling Heterogeneity Within a Class
4.3 Manual or Region Growing Method for Training-Samples Collection
4.4 Extension of Training Samples
4.5 Cognitive Approach to Train Classifier
4.6 Specific Class Mapping Applications
4.7 Summary
Bibliography
Chapter 5 Machine-Learning Models for Specific-Class Mapping
5.1 Introduction
5.2 Fuzzy Set-Theory-Based Algorithms
5.3 Fuzzy c-Means (FCM) Algorithm
5.4 Possibilistic c-Means Classification
5.5 Noise Clustering
5.6 Modified Possibilistic c-Means (MPCM) Algorithm
5.7 Summary
Bibliography
Chapter 6 Learning-Based Algorithms for Specific-Class Mapping
6.1 Introduction
6.2 Convolutional Neural Networks (CNN)
6.3 Recurrent Neural Networks (RNN)
6.4 Difference Between RNN and CNN
6.5 Long Short-Term Memory (LSTM)
6.6 Gated Recurrent Unit (GRU)
6.7 Difference Between GRU & LSTM
6.8 Summary
Bibliography
Appendix A1 Specific Single Class Mapping Case Studies
A1. Fuzzy Versus Deep-Learning Classifiers for Transplanted Paddy Fields Mapping
A2. Dual-Sensor Temporal Data for Mapping Forest Vegetation Species and Specific-Crop Mapping
A3. Handling Heterogeneity With Training Samples Using Individual-Sample-as-Mean Approach for Isabgol (Psyllium Husk) Medicinal Crop
A4. Sunflower Crop Mapping Using Fuzzy Classification While Studying Effect of Red-Edge Bands
A5. Mapping Burnt Paddy Fields Using Two Dates’ Temporal Sentinel-2 Data
A6. Mapping Ten-Year-Old Dalbergia Sissoo Forest Species
A7. Transition Building Footprints Mapping
Appendix A2 SMIC—Temporal Data-Processing Module for Specific-Class Mapping
Index