Multi-Sensor and Multi-Temporal Remote Sensing : Specific Single Class Mapping book cover
1st Edition

Multi-Sensor and Multi-Temporal Remote Sensing
Specific Single Class Mapping



  • Available for pre-order on April 13, 2023. Item will ship after May 4, 2023
ISBN 9781032428321
May 4, 2023 Forthcoming by CRC Press
184 Pages 70 B/W Illustrations

FREE Standard Shipping
USD $99.95

Prices & shipping based on shipping country


Preview

Book Description

This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, importance of dimensionality reduction, various spectral and texture-based indices, single, dual, or multi-sensor temporal sensor concepts, fuzzy machine learning models capable for single class mapping and associated deep learning-based models supported by case studies.

Provides detailed exposition to (hyperspectral and multispectral) remote sensing and related image processing, fuzzy set theoretic image processing and deep learning methods                       

Focusses on use of single, dual, multi-sensor multi-temporal data application for specific single class mapping                                                                                                                       

Reviews pre-processing of multi-sensor multi-temporal remote sensing data set and hyperspectral data set                                                                                                          

Discusses both traditional machine learning and deep learning approaches                                  

Includes case studies from crop mapping, forest species mapping, and stubble burnt paddy fields                      

This book is aimed at researchers and graduate students in remote sensing, image processing, environmental engineering, geomatics, and geoinformatics.

Table of Contents

Contents

Foreword

Preface

Our Gratitude with three R’s

Authors

List of Abbreviations

 

Chapter 1: Remote Sensing Images

1.1 Introduction

1.2 Introduction to Multi-spectral Remote Sensing

1.3 Introduction to Hyper-spectral Remote Sensing

      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 Muti-Sensor Concept

3.6 Indices with CBSI Approach

3.7Summary

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 samples collection

4.4 Extension of training samples

4.5 Cognitive approach to train classifier

4.6 Specific class mapping Applications

4.7Summary

Bibliography

Chapter 5: Machine learning Models for specific class mapping

5.1 Introduction

5.2Fuzzy Set Theory Based Algorithms

5.3 Fuzzy c-Means (FCM) Algorithm

5.4Possibilisticc-Means Classification

5.5 Noise Clustering

5.6 Modified Possibilistic c-Means (MPCM) Algorithm

5.7Summary

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 or LSTM

6.6 Gated Recurrent Unit or GRU

6.7 Difference between GRU & LSTM

6.8Summary

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 Dalbergiasissoo forest specie

A7. Transition building footprints mapping

Appendix A2: SMIC – Temporal Data Processing Module for Specific Class Mapping

...
View More

Author(s)

Biography

Anil Kumar is a scientist/engineer "SG" and the head of photogrammetry and remote sensing department of Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, India. He received his B.Tech degree in civil engineering from IET affiliated to the University of Lucknow, India, and M.E. degree as well as Ph.D in soft computing from the Indian Institute of Technology, Roorkee, India. So far, he has guided eight PhD thesis and eight more are in progress. He has also guided several dissertations of MTech, MSc, BTech, and postgraduate diploma courses. He always love to work with PhD scholars, Master and Graduate students for their research work, and motivate them to adopt research oriented professional carrier. He received the Pisharoth Rama Pisharoty award for contributing state of the art fuzzy based algorithms for earth observation data. His current research interests are in the area of soft computing based machine learning, deep learning for single date and temporal multi-sensor remote sensing data for specific class identification and mapping through in-house development of the SMIC tool. He also works in the area of digital photogrammetry, GPS/GNSS, and LiDAR. He is the author of book ‘Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification’ with CRC Press.

PriyadarshiUpadhyay is working as a Scientist/ Engineer in Uttarakhand Space Application Centre (USAC), Department of Information & Science Technology, Government of Uttarakhand, Dehradun, India. He received his BSc and Msc degree in Physics from Kumaun University, Nainital, India. He completed his M-Tech degree in Remote Sensing from Birla Institute of Technology Mesra, Ranchi, India. He completed his Ph.D. in Geomatics Engineering under Civil Engineering from IIT Roorkee, India. He has guided several graduate and post graduate dissertations in the application area of image processing. He has various research paper in SCI listed peer reviewed journals. He has written book on ‘Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification’ with CRC Press. His research areas are related to application of time series remote sensing, soft computing, machine learning algorithm for specific land cover extraction. He is a life member of 'Indian Society of Remote Sensing' and an associate member of 'The Institution of Engineers, India'.

Uttara Singh an alumna from the University of Allahabad, Prayagraj, is presently working as an Assistant Professor at CMP Degree College, University of Allahabad based in Prayagraj, Uttar Pradesh. Though being a native of U.P., she has travelled far and wide. She has contributed to numerous national and international publications, but her interests lie mainly in urban planning issues and synthesis. She is a life member of several academic societies of repute. She has also guided many PG and UG project dissertations and guiding Post-Doctoral research. Presently she is also holding the office as the course coordinator for ISRO’s sponsored EDUSAT Outreach program for learning Geospatial Techniques and course coordinator for soft skill development programs in the same field in Prayagraj.