Remote Sensing Big Data (Springer Remote Sensing/Photogrammetry)

Contents
About the Authors
Chapter 1: Introduction
	1.1 Concepts of Big Data
	1.2 Features of Big Data
		1.2.1 Big Data Volume
		1.2.2 Big Data Velocity
		1.2.3 Big Data Variety
		1.2.4 Big Data Veracity
		1.2.5 Big Data Value
	1.3 Big Data Method and Technology
	1.4 Remote Sensing Big Data
	References
Chapter 2: Remote Sensing
	2.1 Concepts
	2.2 Sensors
		2.2.1 Sensors by Radiometric Spectrums
			2.2.1.1 Multi- and Hyperspectral Remote Sensing
			2.2.1.2 Active Microwave Remote Sensing
			2.2.1.3 Passive Microwave Remote Sensing
			2.2.1.4 Active Optical Remote Sensing
			2.2.1.5 GPS Remote Sensing
			2.2.1.6 Imaging Sonar
		2.2.2 Sensors by Work Mode
			2.2.2.1 Frame
			2.2.2.2 Whiskbroom
			2.2.2.3 Pushbroom
			2.2.2.4 Side Scanning
			2.2.2.5 Conical Scanning
	2.3 Platforms
		2.3.1 Satellites
		2.3.2 Airborne
		2.3.3 In Situ
		2.3.4 Shipborne
	References
Chapter 3: Special Features of Remote Sensing Big Data
	3.1 Volume of Remote Sensing Big Data
	3.2 Variety of Remote Sensing Big Data
	3.3 Velocity of Remote Sensing Big Data
	3.4 Veracity of Remote Sensing Big Data
	3.5 Value of Remote Sensing Big Data
	References
Chapter 4: Remote Sensing Big Data Collection Challenges and Cyberinfrastructure and Sensor Web Solutions
	4.1 Remote Sensing Big Data Collection Challenges
	4.2 Remote Sensing Big Data Collection Cyberinfrastructure
		4.2.1 Global Earth Observation System of Systems (GEOSS)
		4.2.2 NASA Earth Observing System (EOS) Data and Information System (EOSDIS)
		4.2.3 ESA Federated Earth Observation (FedEO)
	4.3 Sensor Web
	4.4 Applications
		4.4.1 Climate
		4.4.2 Weather
		4.4.3 Disasters
		4.4.4 Agriculture
	References
Chapter 5: Remote Sensing Big Data Computing
	5.1 Computing Power to Handle Big Data: Distributed and Parallel Computing
	5.2 Evolution of Geospatial Computing Platform
		5.2.1 Stand-Alone Software System Architecture
		5.2.2 Client-Server Software System Architecture
		5.2.3 Distributed Computing
	5.3 Service-Oriented Architecture (SOA)
		5.3.1 Service Roles
		5.3.2 Service Operations
		5.3.3 Service Chaining
		5.3.4 Web Services
		5.3.5 Common Technology Stack for Web Services
			5.3.5.1 Web Services Description Language (WSDL)
			5.3.5.2 Universal Description, Discovery, and Integration (UDDI)
			5.3.5.3 The Simple Object Access Protocol (SOAP)
			5.3.5.4 Business Process Execution Language (BPEL)
		5.3.6 Web Service Applications
		5.3.7 Web Service Standards
		5.3.8 OGC Web Services
			5.3.8.1 Operation Components
				5.3.8.1.1 Client Services
				5.3.8.1.2 Catalog and Registry Services
				5.3.8.1.3 Data Services
				5.3.8.1.4 Application Services
			5.3.8.2 Data Components
				5.3.8.2.1 Geospatial Data
				5.3.8.2.2 Geospatial Metadata
				5.3.8.2.3 Names
				5.3.8.2.4 Relationship
				5.3.8.2.5 Containers
	5.4 High-Throughput Computing Infrastructure
		5.4.1 Super Computing
		5.4.2 Cluster Computer
		5.4.3 Grid Computing
		5.4.4 Cloud Computing
			5.4.4.1 What Does the Cloud Provide?
			5.4.4.2 What Make Cloud Possible?
			5.4.4.3 Characteristics of Cloud Computing
			5.4.4.4 Comparing Cloud Computing with Grid Computing
			5.4.4.5 Software Platforms for Distributed Processing of Big Data in Cloud Computing
				5.4.4.5.1 MapReduce with Hadoop
				5.4.4.5.2 Spark
				5.4.4.5.3 SCALE
				5.4.4.5.4 Other Platforms
	References
Chapter 6: Remote Sensing Big Data Management
	6.1 Remote Sensing Big Data Governance
		6.1.1 Strategy
		6.1.2 Organizational Structure/Communications
		6.1.3 Data Policy
		6.1.4 Measurements
		6.1.5 Technology
	6.2 Remote Sensing Big Data Curation
		6.2.1 Remote Sensing Big Data Organization
			6.2.1.1 Data Format
			6.2.1.2 Metadata
			6.2.1.3 Map Projection
		6.2.2 Remote Sensing Big Data Archiving
		6.2.3 Remote Sensing Big Data Cataloging
		6.2.4 Remote Sensing Big Data Quality Assessment
		6.2.5 Remote Sensing Big Data Usability
		6.2.6 Remote Sensing Big Data Version Control
	6.3 Remote Sensing Big Data Dissemination Services
		6.3.1 Data Discovery
		6.3.2 Data Access
	References
Chapter 7: Standards for Big Data Management
	7.1 Standards for Remote Sensing Data Archiving
	7.2 Standards for Remote Sensing Big Data Metadata
		7.2.1 What Is Metadata?
		7.2.2 The FGDC Content Standard for Digital Geospatial Metadata
		7.2.3 The FGDC Remote Sensing Metadata Extensions
		7.2.4 ISO 19115 Geographic Information—Metadata
			7.2.4.1 ISO 19115-2
			7.2.4.2 ISO 19115-1
		7.2.5 ISO Standards for Data Quality
	7.3 Standards for Remote Sensing Big Data Format
	7.4 Standards for Remote Sensing Big Data Discovery
		7.4.1 OGC Catalog Service for Web (CSW)
		7.4.2 OpenSearch
	7.5 Standards for Remote Sensing Big Data Access
		7.5.1 OGC Web Coverage Service (WCS)
		7.5.2 OGC Web Feature Service (WFS)
		7.5.3 OGC Web Map Service (WMS)
		7.5.4 OGC Sensor Observation Service (SOS)
		7.5.5 OpenDAP
	References
Chapter 8: Implementation Examples of Big Data Management Systems for Remote Sensing
	8.1 CWIC
		8.1.1 Introduction
		8.1.2 CEOS WGISS
		8.1.3 CWIC Architecture Design
		8.1.4 CWIC System Implementation
		8.1.5 Results and Conclusion
		8.1.6 Future Work
	8.2 The Registry in GEOSS GCI
		8.2.1 Background
			8.2.1.1 GEO
			8.2.1.2 The Role of the Registry
		8.2.2 The GEOSS Component and Service Registry
			8.2.2.1 Functionalities
			8.2.2.2 Concept
			8.2.2.3 System Design
		8.2.3 System Implementation
			8.2.3.1 Logical Design and Main Functionalities
			8.2.3.2 Registry Pages
			8.2.3.3 The Registry
	References
Chapter 9: Big Data Analytics for Remote Sensing: Concepts and Standards
	9.1 Big Data Analytics Concepts
		9.1.1 What Is Big Data Analytics?
		9.1.2 Categories of Big Data Analytics
		9.1.3 Big Data Analytics Use Cases
	9.2 Remote Sensing Big Data Analytics Concepts
		9.2.1 Remote Sensing Big Data Challenges
		9.2.2 Categories of Remote Sensing Big Data Analytics
		9.2.3 Processes of Remote Sensing Big Data Analytics
		9.2.4 Objectives of Remote Sensing Big Data Analytics
	9.3 Big Data Analytics Standards
		9.3.1 IEEE Big Data Analytics Standards
		9.3.2 ISO Big Data Working Group: ISO/IEC JTC 1/SC 42/WG 2
	References
Chapter 10: Big Data Analytic Platforms
	10.1 Big Data Analytic Platforms
	10.2 Data Storage Strategy in Big Data Analytic Platforms
	10.3 Data-Processing Strategy in Big Data Analytic Platforms
	10.4 Tools in Big Data Analytic Platforms
	10.5 Data Visualization in Big Data Analytic Platforms
	10.6 Remote Sensing Big Data Analytic Platforms
		10.6.1 GeoMesa
		10.6.2 GeoTrellis
		10.6.3 RasterFrames
	10.7 Remote Sensing Big Data Analytic Services
		10.7.1 Google Earth Engine
		10.7.2 EarthServer—an Open Data Cube
		10.7.3 NASA Earth Exchange
		10.7.4 NASA Giovanni
		10.7.5 Others
	References
Chapter 11: Algorithmic Design Considerations of Big Data Analytics
	11.1 Complexity of Remote Sensing Big Data Analytic Algorithms
	11.2 Challenges and Algorithm Design Considerations from Volume
	11.3 Challenges and Algorithm Design Considerations from Velocity
	11.4 Challenges and Algorithm Design Considerations from Variety
	11.5 Challenges and Algorithm Design Considerations from Veracity
	11.6 Challenges and Algorithm Design Considerations from Value
	References
Chapter 12: Machine Learning and Data Mining Algorithms for Geospatial Big Data
	12.1 Distributed and Parallel Learning
	12.2 Data Reduction and Approximate Computing
		12.2.1 Sampling
		12.2.2 Approximate Computing
	12.3 Feature Selection and Feature Extraction
	12.4 Incremental Learning
	12.5 Deep Learning
	12.6 Ensemble Analysis
	12.7 Granular Computing
	12.8 Stochastic Algorithms
	12.9 Transfer Learning
	12.10 Active Learning
	References
Chapter 13: Modeling, Prediction, and Decision Making Based on Remote Sensing Big Data
	13.1 A General Framework
	13.2 Modeling
		13.2.1 Data Models and Structures
		13.2.2 Modeling with Remote Sensing Big Data
		13.2.3 Validation with Remote Sensing Big Data
	13.3 Decision Making
	References
Chapter 14: Examples of Remote Sensing Applications of Big Data Analytics—Fusion of Diverse Earth Observation Data
	14.1 The Concept of Data Fusion
		14.1.1 Definitions
		14.1.2 Classification of Data Fusion
	14.2 Data Fusion Architectures
	14.3 Fusion of MODIS and Landsat with Deep Learning
		14.3.1 The Problem
		14.3.2 Data Fusion Methods
	References
Chapter 15: Examples of Remote Sensing Applications of Big Data Analytics—Agricultural Drought Monitoring and Forecasting
	15.1 Agricultural Drought
	15.2 Remote Sensing Big Data for Agricultural Drought
	15.3 Geospatial Data Analysis Infrastructure GeoBrain
	15.4 The Global Agricultural Drought Monitoring and Forecasting System Portal
	References
Chapter 16: Examples of Remote Sensing Applications of Big Data Analytics—Land Cover Time Series Creation
	16.1 Remote Sensing Big Data for Land Cover Classification
	16.2 Land Cover Classification Methodology
	16.3 Results and Discussions
	References
Chapter 17: Geospatial Big Data Initiatives in the World
	17.1 US Federal Government Big Data Initiative
		17.1.1 Big Earth Data Initiative
		17.1.2 NSF EarthCube
	17.2 Big Data Initiative in China
	17.3 Big Data Initiatives in Europe
	17.4 Big Data Initiatives in Australia
	17.5 Other Big Data Initiatives
	References
Chapter 18: Challenges and Opportunities in the Remote Sensing Big Data
	18.1 Challenges
	18.2 Opportunities
	References
Index