Multi-Sensor and Multi-Temporal Remote Sensing - Anil Kumar, Priyadarshi Upadhyay, Uttara Singh

Multi-Sensor and Multi-Temporal Remote Sensing

Specific Single Class Mapping
Buch | Hardcover
148 Seiten
2023
CRC Press (Verlag)
978-1-032-42832-1 (ISBN)
97,25 inkl. MwSt
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, fuzzy machine learning models supported by case studies.
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.

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.

1. Remote-Sensing Images 2. Evolution of Pixel-Based Spectral Indices 3. Multi-Sensor, Multi-Temporal Remote-Sensing 4. Training Approaches—Role of Training Data 5. Machine-Learning Models for Specific-Class Mapping 6. Learning-Based Algorithms for Specific-Class Mapping Appendix A1 Specific Single Class Mapping Case Studies Appendix A2 SMIC—Temporal Data-Processing Module for Specific-Class Mapping

Erscheinungsdatum
Zusatzinfo 19 Tables, black and white; 22 Line drawings, black and white; 48 Halftones, black and white; 70 Illustrations, black and white
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Gewicht 580 g
Themenwelt Technik Elektrotechnik / Energietechnik
Technik Umwelttechnik / Biotechnologie
ISBN-10 1-032-42832-5 / 1032428325
ISBN-13 978-1-032-42832-1 / 9781032428321
Zustand Neuware
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