Description

Remote sensing deals with the data collection of natural and man-made objects on the earth to show their geographic locations as well as characteristics on the maps. Over the past two decades, there has been an explosive increase in the use and demand of remotely sensed data and algorithms for various types of resources, environmental, and infrastructural development. Remote sensing systems, which provide a global perspective and a wealth of data (panchromatic, multispectral, thermal, hyperspectral, stereo, LiDAR, etc.) about Earth systems at various resolutions, allow users to capture, visualize, and analyze features on the earth’s surface for faster decision making based on the current and future state of land resources. These data products provide costeffective large-coverage data to monitor changes over time for earth-surface processes as well as to correct, update, and maintain GIS databases. With the introduction of computer and automation, new algorithms are being developed which can analyse digital remote sensing data quickly, conomically and accurately, and provide useful information for the application.

Addressing the need for updated information in remote sensing, this book consists of two major divisions; theory and applications. Firstly, different theoretical approaches to understand the acquisition and characteristics of remotely sensed data and their classification approaches are discussed. It provides the basic concept of remote sensing, remote sensing systems, processing, and classification of satellite image using the latest techniques, such as Artificial Neural Network, Convolution Neural Network, k-NN, Object-based Image Analysis, Fuzzy C-Means, Artificial Intelligence, Machine Learning, Deep Learning, Support Vector Machine, Random Forest, Decision Tree, etc. These modern methods are useful not only in accurate classification and extraction of specific objects from images but also for developing the innovative solutions. The book also provides a framework for utilizing vast amount of remote sensing data and software freely available on open platform. Secondly, based on theoretical and methodological issues, a large portion of book is dedicated to showcase a series of aapplication areas involving the use of remote sensing data. Each application area starts with an analysis of state-of-the-art methodology followed by a detailed presentation of a case study. The application areas include landuse and land-cover mapping, agriculture, hydrology, disaster mapping and management, feature extraction, and smart city. The book also discusses the limitation, opportunities and applications of remote sensing in future.

The book covers the course contents required mainly for UG (Civil Engg.) and PG (Geoinformatics) as well as students, research scholars, faculty members and professionals of Computer Science, Information Technology, Data Science, Geology, Geography, Environmental Science and Earth Science, Today, remote sensing has found large number of applications in civil engg., natural resources management, disaster management, environment, geology, soil, forestry, agriculture, urban, infrastructure development, 3D mapping, public health, visualization, etc. The readers having interest in remote sensing will find the book useful as it has been written in simple language and topics discussed with plenty of tabular data and fTheory

Basics of Remote Sensing

Electromagnetic Radiations and Interaction with Atmosphere

Various Remote Sensing Sensors and Data Characteristics

Various Remote Sensing Platforms

Image Pre-processing Approaches

Image Classification

The State-of-Art Classification Techniques

Applications

Applications of Remote Sensing

Land Use and Land Cover Mapping and Modelling

Remote Sensing Platforms for Agricultural Applications

Disaster Monitoring and Management Using Remote Sensing Technology

Remote Sensing of Snow Cover

Feature/Object Extraction from Remote Sensing Algorithms

Applying Remote Sensing for Smart City

The Future of Remote Sensing