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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 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.
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Autorenporträt
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.
Inhaltsangabe
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
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
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