Cloud Computing In Remote Sensing
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Cloud Computing in Remote Sensing
This book provides the users with quick and easy data acquisition, processing, storage and product generation services. It describes the entire life cycle of remote sensing data and builds an entire high performance remote sensing data processing system framework. It also develops a series of remote sensing data management and processing standards. Features: Covers remote sensing cloud computing Covers remote sensing data integration across distributed data centers Covers cloud storage based remote sensing data share service Covers high performance remote sensing data processing Covers distributed remote sensing products analysis
Cloud Computing for Geospatial Big Data Analytics
This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.
Remotely Sensed Data Characterization, Classification, and Accuracies
Author: Ph.D., Prasad S. Thenkabail
language: en
Publisher: CRC Press
Release Date: 2015-10-02
A volume in the Remote Sensing Handbook series, Remotely Sensed Data Characterization, Classification, and Accuracies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, and Remote Sensing of Water Resources, Disasters, and Urban Studies. This volume demonstrates the experience, utility, methods, and models used in studying a wide array of remotely sensed data characterization, classification, and accuracies for terrestrial applications. Leading experts on global geographic coverage, study areas, and array of satellite and sensors contribute to this unique handbook. This theoretical as well as highly practical book represents a thorough history of advancement in the field over last 50 years, bringing us to where we are now, and highlighting future possibilities. Highlights include: Fundamental and advanced topics in remote-sensing satellites and sensors Remote sensing data calibration, normalization, harmonization, and synthesis Optical, Radar, LiDAR, thermal, hyperspectral, and other satellite sensors, normalization of remotely sensed data, and data degradations Digital image processing, urban image classification, and image classification methods in land use\land cover, cropland, change detection studies Enhanced vegetation indices and standardization of vegetation indices Object-based image analysis (OBIA) and geospatial data integration LiDAR data processing and applications Geoprocessing, GIS, and GIScience GNSS applications Crowdsourcing and cloud computing Google Earth for Earth Sciences Map accuracies Remote-sensing law or space law, and a host of other topics.