My Account
Log in Registration
All
Cart
Highlights
Fiction Books
Non-Fiction Books
Children's Books
Rare Books
Music
DVD & Blu-Ray
Video Games
Category
Wishlists
Cart
All
Free US shipping over $10
Trustpilot
Proud to be B-Corp

Earth Observation Data Analytics Using Machine and Deep Learning Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)

Earth Observation Data Analytics Using Machine and Deep Learning By Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)

Earth Observation Data Analytics Using Machine and Deep Learning by Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)

Reviews:
$184.39
Condition - New
Only 2 left
New

Summary

Using machine and deep learning techniques the authors introduce pre-processing methods applied to satellite images to identify land cover features, detect object, classify crops, recognize targets, and monitor and support earth resources. Readers will need a basic understanding of computing, remote sensing and image interpretation.

Earth Observation Data Analytics Using Machine and Deep Learning Summary

Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges by Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)

Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and multi-temporal observations.

Approaches for applying pre-processing methods and deep learning techniques to satellite images for various applications - such as identifying land cover features, object detection, crop classification, target recognition, and the monitoring of earth resources - are described. Cost-efficient resource allocation solutions are provided, which are robust against common uncertainties that occur in annotating and extracting features on satellite images.

This book is a valuable resource for engineers and researchers in academia and industry working on AI, machine and deep learning, data science, remote sensing, GIS, SAR, satellite communications, space science, image processing and computer vision. It will also be of interest to staff at research agencies, lecturers and advanced students in related fields. Readers will need a basic understanding of computing, remote sensing, GIS and image interpretation.

About Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)

Sanjay Garg is a professor in the Department of Computer Science and Engineering at the Jaypee University of Engineering and Technology, India. His research interests include data science, algorithms and pattern recognition. He has over 30 years of academic and research experience. He has completed six funded research projects sponsored by ISRO under the RESPOND scheme and GUJCOST as principal investigator in the field of earth observation data analytics and image processing. He has supervised 10 doctoral dissertations in the same field. He is a fellow of the Institution of Engineers (India), a senior member of IEEE and a Senior member of ACM. Swati Jain is an associate professor in the Department of Computer Science and Engineering at the Institute of Technology, Nirma University, India. She works in the areas of machine learning, data analytics, and deep learning. She is currently working on four funded research projects, two of them under BRNS Department of Atomic Energy and two by ISRO (Indian Space Research Organization). She has completed one funded research project under ISRO-RESPOND as a CO-PI. She is actively working on establishing a Center of Excellence in-Data Science in association with Binghamton University, USA. She received her PhD degree from Nirma University in the areas of machine learning and image processing. Nitant Dube is the group director of MOSDAC Research at the joint Space Applications Centre (SAC) and ISRO, India. His research fields include satellite image processing, big data analytics, AI/ML and its applications for Earth observation data, geo-intelligence and web-based processing. He is involved in the design and development of software for meteorological and oceanographic applications. He has been responsible for the design and development of data products and information processing systems for Indian remote sensing satellites and has contributed towards the development and operationalization of data processing systems at Indian and International ground stations. He is an ISRO nominated member for the CEOS Working Group on Information System and Services (WGISS). He holds a PhD degree in Computer Science from Nirma University, Ahmedabad (GJ), India. Nebu Varghese is an assistant manager (GIS) in the Land and Municipal Service at Dholera Industrial City Development Limited (DICDL), India. He works in the areas of GIS systems analysis and prepare design for new GIS methodologies, land use mapping, land cover mapping, urban land use analysis, spatial data management, satellite image processing and analysis, machine learning and deep learning. Currently, he is working on regional and city-level planning projects, where he employs the most cutting-edge technologies for building information model (BIM) to GIS Integration with micro-level asset information of all Infrastructure in city development. He has been involved in various government DST, ISRO, and IIMA funded projects and was also involved in the innovation hub Malawi project with the Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ), India. He is a member of ISPRS. He holds a master's degree in remote sensing & GIS from Sam Higginbottom University of Agriculture, Technology & Sciences (SHUATS), Prayagraj (UP), India.

Table of Contents

  • Chapter 1: Introduction
  • Part I: Clustering and classification of Earth Observation data
    • Chapter 2: Deep learning method for crop classification using remote sensing data
    • Chapter 3: Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cover estimation
    • Chapter 4: Leveraging twin networks for land use land cover classification
    • Chapter 5: Exploiting artificial immune networks for enhancing RS image classification
    • Chapter 6: Detection and segmentation of aircrafts in UAV images with a deep learning-based approach
  • Part II: Rare event detection using Earth Observation data
    • Chapter 7: A transfer learning approach for hurricane damage assessment using satellite imagery
    • Chapter 8: Wildfires, volcanoes and climate change monitoring from satellite images using deep neural networks
    • Chapter 9: A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study of an Indian state
    • Chapter 10: Machine learning paradigm for predicting reservoir property: an exploratory analysis
  • Part III: Tools and technologies for Earth Observation data
    • Chapter 11: The application of R software in water science
    • Chapter 12: Geospatial big data analysis using neural networks
    • Chapter 13: Software framework for spatiotemporal data analysis and mining of earth observation data
    • Chapter 14: Conclusion

Additional information

NPB9781839536175
9781839536175
1839536179
Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges by Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)
Sanjay Garg (Professor, Jaypee University of Engineering and Technology, Computer Science and Engineering Department, India)
Computing and Networks
New
Hardback
Institution of Engineering and Technology
2023-07-11
258
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a new book - be the first to read this copy. With untouched pages and a perfect binding, your brand new copy is ready to be opened for the first time

Customer Reviews - Earth Observation Data Analytics Using Machine and Deep Learning