Cloud Computing in Remote Sensing

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
1. Remote Sensing and Cloud Computing
	1.1 Remote Sensing
		1.1.1 Remote sensing definition
		1.1.2 Remote sensing big data
		1.1.3 Applications of remote sensing big data
		1.1.4 Challenges of remote sensing big data
			1.1.4.1 Data integration challenges
			1.1.4.2 Data processing challenges
	1.2 Cloud Computing
		1.2.1 Cloud service models
		1.2.2 Cloud deployment models
		1.2.3 Security in the Cloud
		1.2.4 Open-source Cloud frameworks
			1.2.4.1 OpenStack
			1.2.4.2 Apache CloudStack
			1.2.4.3 OpenNebula
		1.2.5 Big data in the Cloud
			1.2.5.1 Big data management in the Cloud
			1.2.5.2 Big data analytics in the Cloud
	1.3 Cloud Computing in Remote Sensing
2. Remote Sensing Data Integration in a Cloud Computing Environment
	2.1 Introduction
	2.2 Background on Architectures for Remote Sensing Data Integration
		2.2.1 Distributed integration of remote sensing data
		2.2.2 OODT: a data integration framework
	2.3 Distributed Integration of Multi-Source Remote Sensing Data
		2.3.1 The ISO 19115-based metadata transformation
		2.3.2 Distributed multi-source remote sensing data integration
	2.4 Experiment and Analysis
	2.5 Conclusions
3. Remote Sensing Data Organization and Management in a Cloud Computing Environment
	3.1 Introduction
	3.2 Preliminaries and Related Techniques
		3.2.1 Spatial organization of remote sensing data
		3.2.2 MapReduce and Hadoop
		3.2.3 HBase
		3.2.4 Elasticsearch
	3.3 LSI Organization Model of Multi-Source Remote Sensing Data
	3.4 Remote Sensing Big Data Management in a Parallel File System
		3.4.1 Full-text index of multi-source remote sensing metadata
		3.4.2 Distributed data retrieval
	3.5 Remote Sensing Big Data Management in the Hadoop Ecosystem
		3.5.1 Data organization and storage component
		3.5.2 Data index and search component
	3.6 Metadata Retrieval Experiments in a Parallel File System
		3.6.1 LSI model-based metadata retrieval experiments in a parallel File system
		3.6.2 Comparative experiments and analysis
			3.6.2.1 Comparative experiments
			3.6.2.2 Results analysis
	3.7 Metadata Retrieval Experiments in the Hadoop Ecosystem
		3.7.1 Time comparisons of storing metadata in HBase
		3.7.2 Time comparisons of loading metadata from HBase to Elasticsearch
	3.8 Conclusions
4. High Performance Remote Sensing Data Processing in a Cloud Computing Environment
	4.1 Introduction
	4.2 High Performance Computing for RS Big Data: State of the Art
		4.2.1 Cluster computing for RS data processing
		4.2.2 Cloud computing for RS data processing
			4.2.2.1 Programming models for big data
			4.2.2.2 Resource management and provisioning
	4.3 Requirements and Challenges: RSCloud for RS Big Data
	4.4 pipsCloud: High Performance Remote Sensing Clouds
		4.4.1 The system architecture of pipsCloud
		4.4.2 RS data management and sharing
			4.4.2.1 HPGFS: distributed RS data storage with application aware data layouts and copies
			4.4.2.2 RS metadata management with NoSQL database
			4.4.2.3 RS data index with Hilbert R+tree
			4.4.2.4 RS data subscription and distribution
		4.4.3 VE-RS: RS-specific HPC environment as a service
			4.4.3.1 On-demand HPC cluster platforms with bare-metal provisioning
			4.4.3.2 Skeletal programming for RS big data processing
		4.4.4 VS-RS: Cloud-enabled RS data processing system
			4.4.4.1 Dynamic workflow processing for RS applications in the Cloud
	4.5 Experiments and Discussion
	4.6 Conclusions
5. Programming Technologies for High Performance Remote Sensing Data Processing in a Cloud Computing Environment
	5.1 Introduction
	5.2 Related Work
	5.3 Problem Definition
		5.3.1 Massive RS data
		5.3.2 Parallel programmability
		5.3.3 Data processing speed
	5.4 Design and Implementation
		5.4.1 Generic algorithm skeletons for remote sensing applications
			5.4.1.1 Categories of remote sensing algorithms
			5.4.1.2 Generic RS farm-pipeline skeleton
			5.4.1.3 Generic RS image-wrapper skeleton
			5.4.1.4 Generic feature abstract skeleton
		5.4.2 Distributed RS data templates
			5.4.2.1 RSData templates
			5.4.2.2 Dist_RSData templates
	5.5 Experiments and Discussion
	5.6 Conclusions
6. Construction and Management of Remote Sensing Production Infrastructures across Multiple Satellite Data Centers
	6.1 Introduction
	6.2 Related Work
	6.3 Infrastructures Overview
		6.3.1 Target environment
		6.3.2 MDCPS infrastructures overview
	6.4 Design and Implementation
		6.4.1 Data management
			6.4.1.1 Spatial metadata management for co-processing
			6.4.1.2 Distributed le management
		6.4.2 Workflow management
			6.4.2.1 Workflow construction
			6.4.2.2 Task scheduling
			6.4.2.3 Workflow fault-tolerance
	6.5 Experiments
		6.5.1 Related experiments on dynamic data management
		6.5.2 Related experiments on workflow management
	6.6 Discussion
		6.6.1 System architecture
		6.6.2 System feasibility
		6.6.3 System scalability
	6.7 Conclusions and Future Work
7. Remote Sensing Product Production in an OpenStack-Based Cloud Computing Environment
	7.1 Introduction
	7.2 Background and Related Work
		7.2.1 Remote sensing products
			7.2.1.1 Fine processing products
			7.2.1.2 Inversion index products
			7.2.1.3 Thematic products
		7.2.2 Remote sensing production system
	7.3 Cloud-Based Remote Sensing Production System
		7.3.1 Program framework
		7.3.2 System architecture
		7.3.3 Knowledge base and inference rules
			7.3.3.1 The upper and lower hierarchical relationship database
			7.3.3.2 Input/output database of every kind of remote sensing product
			7.3.3.3 Inference rules for production demand data selection
			7.3.3.4 Inference rules for workflow organization
		7.3.4 Business logic
		7.3.5 Active service patterns
	7.4 Experiment and Case Study
		7.4.1 Global scale remote sensing production
		7.4.2 Regional scale mosaic production
		7.4.3 Local scale change detection
			7.4.3.1 Remote sensing data cube
			7.4.3.2 Local scale time-series production
	7.5 Conclusions
8. Knowledge Discovery and Information Analysis from Remote Sensing Big Data
	8.1 Introduction
	8.2 Preliminaries and Related Work
		8.2.1 Knowledge discovery categories
		8.2.2 Knowledge discovery methods
		8.2.3 Related work
	8.3 Architecture Overview
		8.3.1 Target data and environment
		8.3.2 FRSDC architecture overview
	8.4 Design and Implementation
		8.4.1 Feature data cube
			8.4.1.1 Spatial feature object in FRSDC
			8.4.1.2 Data management
		8.4.2 Distributed executed engine
	8.5 Experiments
	8.6 Conclusions
9. Automatic Construction of Cloud Computing Infrastructures in Remote Sensing
	9.1 Introduction
	9.2 Definition of the Remote Sensing Oriented Cloud Computing Infrastructure
		9.2.1 Generally used cloud computing infrastructure
		9.2.2 Remote sensing theme oriented cloud computing infrastructure
	9.3 Design and Implementation of Remote Sensing Oriented Cloud Computing Infrastructure
		9.3.1 System architecture design
		9.3.2 System workflow design
		9.3.3 System module design
	9.4 Key Technologies of Remote Sensing Oriented Cloud Infrastructure Automatic Construction
		9.4.1 Automatic deployment based on OpenStack and Salt-Stack
		9.4.2 Resource monitoring based on Ganglia
	9.5 Conclusions
10. Security Management in a Remote-Sensing-Oriented Cloud Computing Environment
	10.1 Introduction
	10.2 User Behavior Authentication Scheme
		10.2.1 User behavior authentication set
		10.2.2 User behavior authentication process
	10.3 The Method for User Behavior Trust Level Prediction
		10.3.1 Bayesian network model for user behavior trust prediction
		10.3.2 The calculation method of user behavior prediction
			10.3.2.1 Prior probability calculation of user behavior attribute level
			10.3.2.2 Conditional probability of behavioral authentication set
			10.3.2.3 Method of calculating behavioral trust level
		10.3.3 User behavior trust level prediction example and analysis
	10.4 Conclusions
11. A Cloud-Based Remote Sensing Information Service System Design and Implementation
	11.1 Introduction
	11.2 Remote Sensing Information Service Mode Design
		11.2.1 Overall process of remote sensing information service mode
		11.2.2 Service mode design of RSDaaS
		11.2.3 Service mode design of RSDPaaS
		11.2.4 Service mode design of RSPPaaS
		11.2.5 Service mode design of RSCPaaS
	11.3 Architecture Design
	11.4 Functional Module Design
		11.4.1 Function module design of RSDaaS
		11.4.2 Function module design of RSDPaaS
		11.4.3 Function module design of RSPPaaS
		11.4.4 Function module design of RSCPaaS
	11.5 Prototype System Design and Implementation
		11.5.1 RSDaaS subsystem
		11.5.2 RSDPaaS subsystem
		11.5.3 RSPPaaS subsystem
		11.5.4 RSCPaaS subsystem
	11.6 Conclusions
Bibliography
Index