1st Edition

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

    178 Pages 70 B/W Illustrations
    by CRC Press

    This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields.

    Key features:

    • Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes
    • Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise
    • Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI)
    • Discusses the role of training data to handle the heterogeneity within a class
    • Supports multi-sensor and multi-temporal data processing through in-house SMIC software
    • Includes case studies and practical applications for single class mapping

    This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.


    1 Remote-Sensing Images

    1.1 Introduction

    1.2 Introduction to Multispectral Remote-Sensing

    1.3 Introduction to Hyperspectral Remote-Sensing

    1.4 Introduction to SAR Remote-Sensing

    1.5 Dimensionality Reduction

    1.6 Summary


    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


    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


    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


    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


    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


    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


    Anil Kumar is a scientist/engineer ‘SG’ and the head of the 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 with the University of Lucknow, India, and his M.E. degree, as well as his Ph.D. in soft computing, from the Indian Institute of Technology, Roorkee, India. So far, he has guided eight Ph.D. thesis, and eight more are in progress. He has also guided several dissertations of M.Tech., M.Sc., B.Tech., and postgraduate diploma courses. He always loves to work with Ph.D. scholars and masters and graduate students for their research work, and to motivate them to adopt research-oriented professional careers. 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 areas 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 the 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 the book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification with CRC Press.

    Priyadarshi Upadhyay 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 B.Sc. and M.Sc. degrees 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 papers in SCI-listed, peer-reviewed journals. He has written the book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification with CRC Press. His research areas are related to the application of time-series remote-sensing, soft computing, and machine-learning algorithms for specific land-cover extraction. He is a life member of the Indian Society of Remote Sensing and is 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 to name a few Indian National Cartographic Association (INCA), Indian Institute of Geomorphologist (IGI), National Association of Geographers (NAGI). She has also guided many PG and UG project dissertations and has guided post-doctoral research. Presently, she also holds the office of the course coordinator for ISRO’s sponsored EDUSAT Outreach program for learning geospatial techniques and the course coordinator for soft-skill development programs in the same field in Prayagraj.