Remote Sensing: Theory and Applications

This book explores the world of remote sensing technology, offering comprehensive insights into its principles, data acquisition methods, advanced processing techniques, and diverse applications. It covers the basics of remote sensing such as the foundational principles and data acquisition techniques, image pre-processing, such as noise removal, radiometric corrections, and image fusion, and advanced classification techniques like machine learning algorithms including neural networks and support vector machines. Finally, it discusses disaster management and agriculture, demonstrating how remote sensing methods are revolutionizing fields such as disaster response and agricultural monitoring. Professionals, researchers, and students involved in environmental sciences, geography, urban planning, and disaster management will benefit from these topics.FEATURES • Explores cutting-edge advanced classification techniques like machine learning algorithms including neural networks and support vector machines• Covers both the theoretical principles and also practical data acquisition techniques, image pre-processing, noise removal, radiometric corrections, and image fusion• Demonstrates how remote sensing methods are revolutionizing fields such as disaster response and agricultural monitoring• Includes companion files for downloading with full color images from the text (available for downloading with Amazon proof of purchase by writing to the publisher at info@merclearning.com)TABLE OF CONTENTS1: Basics of Remote Sensing. 2: Electromagnetic Radiations and Interaction with the Atmosphere. 3: Various Remote Sensing Sensors and Data Characteristics. 4: Various Remote Sensing Platforms. 5: Image Pre-processing Approaches.6: Image Classification. 7: The State-of-the-Art Classification Techniques. 8: Applications of Remote Sensing. 9: Land Use and Land Cover Mapping and Modelling.10: Remote Sensing Platforms for Agricultural Applications.11: Disaster Monitoring and Management Using Remote Sensing Technology. 12: Remote Sensing of Snow Cover.13: Feature/Object Extraction from Remote Sensing Algorithms. 14: Applying Remote Sensing for a Smart City.15: The Future of Remote Sensing. References. Index.ABOUT THE AUTHORP. K. Garg holds a PhD in remote sensing (University of Bristol) and is a professor of civil engineering, specializing in geomatics engineering. He is a member of the advisory committee of CSSTEAP (Centre for Space Scienceand Technology Education in Asia and the Pacific). Read more