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E-raamat: Cloud Computing in Remote Sensing [Taylor & Francis e-raamat]

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  • Formaat: 292 pages, 20 Tables, black and white; 129 Illustrations, black and white
  • Ilmumisaeg: 28-Jun-2019
  • Kirjastus: CRC Press
  • ISBN-13: 9780429488764
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  • Taylor & Francis e-raamat
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Cloud Computing in Remote SensingSuurem pilt
  • Formaat: 292 pages, 20 Tables, black and white; 129 Illustrations, black and white
  • Ilmumisaeg: 28-Jun-2019
  • Kirjastus: CRC Press
  • ISBN-13: 9780429488764
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429488764

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
Preface xi
1 Remote Sensing and Cloud Computing
1(16)
1.1 Remote Sensing
1(5)
1.1.1 Remote sensing definition
1(1)
1.1.2 Remote sensing big data
2(1)
1.1.3 Applications of remote sensing big data
3(2)
1.1.4 Challenges of remote sensing big data
5(1)
1.1.4.1 Data integration challenges
5(1)
1.1.4.2 Data processing challenges
5(1)
1.2 Cloud Computing
6(8)
1.2.1 Cloud service models
6(1)
1.2.2 Cloud deployment models
7(1)
1.2.3 Security in the Cloud
7(1)
1.2.4 Open-source Cloud frameworks
8(1)
1.2.4.1 OpenStack
8(2)
1.2.4.2 Apache CloudStack
10(1)
1.2.4.3 OpenNebula
10(2)
1.2.5 Big data in the Cloud
12(1)
1.2.5.1 Big data management in the Cloud
12(1)
1.2.5.2 Big data analytics in the Cloud
12(2)
1.3 Cloud Computing in Remote Sensing
14(3)
2 Remote Sensing Data Integration in a Cloud Computing Environment
17(12)
2.1 Introduction
17(1)
2.2 Background on Architectures for Remote Sensing Data Integration
18(2)
2.2.1 Distributed integration of remote sensing data
18(1)
2.2.2 OODT: a data integration framework
19(1)
2.3 Distributed Integration of Multi-Source Remote Sensing Data
20(4)
2.3.1 The ISO 19115-based metadata transformation
20(2)
2.3.2 Distributed multi-source remote sensing data integration
22(2)
2.4 Experiment and Analysis
24(3)
2.5 Conclusions
27(2)
3 Remote Sensing Data Organization and Management in a Cloud Computing Environment
29(26)
3.1 Introduction
29(2)
3.2 Preliminaries and Related Techniques
31(4)
3.2.1 Spatial organization of remote sensing data
31(1)
3.2.2 MapReduce and Hadoop
32(1)
3.2.3 HBase
33(1)
3.2.4 Elasticsearch
33(2)
3.3 LSI Organization Model of Multi-Source Remote Sensing Data
35(3)
3.4 Remote Sensing Big Data Management in a Parallel File System
38(4)
3.4.1 Full-text index of multi-source remote sensing metadata
38(2)
3.4.2 Distributed data retrieval
40(2)
3.5 Remote Sensing Big Data Management in the Hadoop Ecosystem
42(3)
3.5.1 Data organization and storage component
42(1)
3.5.2 Data index and search component
43(2)
3.6 Metadata Retrieval Experiments in a Parallel File System
45(6)
3.6.1 LSI model-based metadata retrieval experiments in a parallel file system
45(3)
3.6.2 Comparative experiments and analysis
48(1)
3.6.2.1 Comparative experiments
48(1)
3.6.2.2 Results analysis
49(2)
3.7 Metadata Retrieval Experiments in the Hadoop Ecosystem
51(2)
3.7.1 Time comparisons of storing metadata in HBase
52(1)
3.7.2 Time comparisons of loading metadata from HBase to Elasticsearch
52(1)
3.8 Conclusions
53(2)
4 High Performance Remote Sensing Data Processing in a Cloud Computing Environment
55(34)
4.1 Introduction
56(2)
4.2 High Performance Computing for RS Big Data: State of the Art
58(3)
4.2.1 Cluster computing for RS data processing
58(1)
4.2.2 Cloud computing for RS data processing
59(1)
4.2.2.1 Programming models for big data
60(1)
4.2.2.2 Resource management and provisioning
60(1)
4.3 Requirements and Challenges: RSCloud for RS Big Data
61(1)
4.4 pipsCloud: High Performance Remote Sensing Clouds
62(20)
4.4.1 The system architecture of pipsCloud
63(2)
4.4.2 RS data management and sharing
65(2)
4.4.2.1 HPGFS: distributed RS data storage with application-aware data layouts and copies
67(1)
4.4.2.2 RS metadata management with NoSQL database
68(1)
4.4.2.3 RS data index with Hilbert R+tree
69(2)
4.4.2.4 RS data subscription and distribution
71(1)
4.4.3 VE-RS: RS-specific HPC environment as a service
72(1)
4.4.3.1 On-demand HPC cluster platforms with bare-metal provisioning
73(3)
4.4.3.2 Skeletal programming for RS big data processing
76(1)
4.4.4 VS-RS: Cloud-enabled RS data processing system
77(1)
4.4.4.1 Dynamic workflow processing for RS applications in the Cloud
78(4)
4.5 Experiments and Discussion
82(3)
4.6 Conclusions
85(4)
5 Programming Technologies for High Performance Remote Sensing Data Processing in a Cloud Computing Environment
89(32)
5.1 Introduction
89(2)
5.2 Related Work
91(1)
5.3 Problem Definition
92(2)
5.3.1 Massive RS data
92(1)
5.3.2 Parallel programmability
93(1)
5.3.3 Data processing speed
94(1)
5.4 Design and Implementation
94(21)
5.4.1 Generic algorithm skeletons for remote sensing applications
97(1)
5.4.1.1 Categories of remote sensing algorithms
98(1)
5.4.1.2 Generic RS farm-pipeline skeleton
98(4)
5.4.1.3 Generic RS image-wrapper skeleton
102(3)
5.4.1.4 Generic feature abstract skeleton
105(3)
5.4.2 Distributed RS data templates
108(1)
5.4.2.1 RSData templates
108(3)
5.4.2.2 Dist-RSData templates
111(4)
5.5 Experiments and Discussion
115(5)
5.6 Conclusions
120(1)
6 Construction and Management of Remote Sensing Production Infrastructures across Multiple Satellite Data Centers
121(30)
6.1 Introduction
121(2)
6.2 Related Work
123(1)
6.3 Infrastructures Overview
124(4)
6.3.1 Target environment
124(1)
6.3.2 MDCPS infrastructures overview
125(3)
6.4 Design and Implementation
128(13)
6.4.1 Data management
128(2)
6.4.1.1 Spatial metadata management for co-processing
130(1)
6.4.1.2 Distributed file management
131(2)
6.4.2 Workflow management
133(3)
6.4.2.1 Workflow construction
136(1)
6.4.2.2 Task scheduling
137(4)
6.4.2.3 Workflow fault-tolerance
141(1)
6.5 Experiments
141(6)
6.5.1 Related experiments on dynamic data management
142(4)
6.5.2 Related experiments on workflow management
146(1)
6.6 Discussion
147(1)
6.6.1 System architecture
147(1)
6.6.2 System feasibility
148(1)
6.6.3 System scalability
148(1)
6.7 Conclusions and Future Work
148(3)
7 Remote Sensing Product Production in an OpenStack-Based Cloud Computing Environment
151(24)
7.1 Introduction
152(1)
7.2 Background and Related Work
153(3)
7.2.1 Remote sensing products
153(1)
7.2.1.1 Fine processing products
154(1)
7.2.1.2 Inversion index products
154(1)
7.2.1.3 Thematic products
154(1)
7.2.2 Remote sensing production system
155(1)
7.3 Cloud-Based Remote Sensing Production System
156(11)
7.3.1 Program framework
156(1)
7.3.2 System architecture
157(2)
7.3.3 Knowledge base and inference rules
159(1)
7.3.3.1 The upper and lower hierarchical relationship database
159(1)
7.3.3.2 Input/output database of every kind of remote sensing product
160(1)
7.3.3.3 Inference rules for production demand data selection
161(1)
7.3.3.4 Inference rules for workflow organization
161(1)
7.3.4 Business logic
162(3)
7.3.5 Active service patterns
165(2)
7.4 Experiment and Case Study
167(4)
7.4.1 Global scale remote sensing production
167(1)
7.4.2 Regional scale mosaic production
168(2)
7.4.3 Local scale change detection
170(1)
7.4.3.1 Remote sensing data cube
171(1)
7.4.3.2 Local scale time-series production
171(1)
7.5 Conclusions
171(4)
8 Knowledge Discovery and Information Analysis from Remote Sensing Big Data
175(16)
8.1 Introduction
175(1)
8.2 Preliminaries and Related Work
176(4)
8.2.1 Knowledge discovery categories
176(2)
8.2.2 Knowledge discovery methods
178(1)
8.2.3 Related work
179(1)
8.3 Architecture Overview
180(2)
8.3.1 Target data and environment
180(1)
8.3.2 FRSDC architecture overview
181(1)
8.4 Design and Implementation
182(4)
8.4.1 Feature data cube
182(1)
8.4.1.1 Spatial feature object in FRSDC
182(1)
8.4.1.2 Data management
182(2)
8.4.2 Distributed executed engine
184(2)
8.5 Experiments
186(3)
8.6 Conclusions
189(2)
9 Automatic Construction of Cloud Computing Infrastructures in Remote Sensing
191(16)
9.1 Introduction
191(1)
9.2 Definition of the Remote Sensing Oriented Cloud Computing Infrastructure
192(3)
9.2.1 Generally used cloud computing infrastructure
193(1)
9.2.2 Remote sensing theme oriented cloud computing infrastructure
193(2)
9.3 Design and Implementation of Remote Sensing Oriented Cloud Computing Infrastructure
195(5)
9.3.1 System architecture design
195(1)
9.3.2 System workflow design
196(2)
9.3.3 System module design
198(2)
9.4 Key Technologies of Remote Sensing Oriented Cloud Infrastructure Automatic Construction
200(5)
9.4.1 Automatic deployment based on OpenStack and Salt-Stack
200(3)
9.4.2 Resource monitoring based on Ganglia
203(2)
9.5 Conclusions
205(2)
10 Security Management in a Remote-Sensing-Oriented Cloud Computing Environment
207(14)
10.1 Introduction
207(2)
10.2 User Behavior Authentication Scheme
209(4)
10.2.1 User behavior authentication set
209(1)
10.2.2 User behavior authentication process
210(3)
10.3 The Method for User Behavior Trust Level Prediction
213(7)
10.3.1 Bayesian network model for user behavior trust prediction
213(1)
10.3.2 The calculation method of user behavior prediction
214(1)
10.3.2.1 Prior probability calculation of user behavior attribute level
214(1)
10.3.2.2 Conditional probability of behavioral authentication set
215(1)
10.3.2.3 Method of calculating behavioral trust level
216(1)
10.3.3 User behavior trust level prediction example and analysis
216(4)
10.4 Conclusions
220(1)
11 A Cloud-Based Remote Sensing Information Service System Design and Implementation
221(26)
11.1 Introduction
221(2)
11.2 Remote Sensing Information Service Mode Design
223(6)
11.2.1 Overall process of remote sensing information service mode
223(1)
11.2.2 Service mode design of RSDaaS
224(1)
11.2.3 Service mode design of RSDPaaS
225(1)
11.2.4 Service mode design of RSPPaaS
226(2)
11.2.5 Service mode design of RSCPaaS
228(1)
11.3 Architecture Design
229(4)
11.4 Functional Module Design
233(5)
11.4.1 Function module design of RSDaaS
233(1)
11.4.2 Function module design of RSDPaaS
234(1)
11.4.3 Function module design of RSPPaaS
235(2)
11.4.4 Function module design of RSCPaaS
237(1)
11.5 Prototype System Design and Implementation
238(7)
11.5.1 RSDaaS subsystem
240(2)
11.5.2 RSDPaaS subsystem
242(1)
11.5.3 RSPPaaS subsystem
243(1)
11.5.4 RSCPaaS subsystem
244(1)
11.6 Conclusions
245(2)
Bibliography 247(32)
Index 279
Dr. Lizhe Wang is a "ChuTian" Chair Professor at School of Computer Science, China Univ. of Geosciences (CUG), and a Professor at Inst. of Remote Sensing & Digital Earth, Chinese Academy of Sciences (CAS). Prof. Wang received B.E. & M.E from Tsinghua Univ. and Doctor of Eng. from Univ. Karlsruhe (Magna Cum Laude), Germany. Prof. Wang is a Fellow of IET, Fellow of British Computer Society. Prof. Wang serves as an Associate Editor of IEEE TPDS, TCC and TSUSC. His main research interests include HPC, e-Science, and remote sensing image processing.









List of Publications:



1. Lizhe Wang, Dan Chen, Wangyang Liu, Yan Ma, Yanhui Wu, Ze Deng: DDDAS-Based Parallel Simulation of Threat Management for Urban Water Distribution Systems. Computing in Science and Engineering 16(1): 8-17 (2014)



2. Jining Yan, Lizhe Wang, Lajiao Chen, Lingjun Zhao, Bormin Huang: A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea. Remote Sensing 7(6): 7105-7125 (2015)



3. Boxin Zuo, Lizhe Wang, Weitao Chen: Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic Sources. Sensors 17(9): 1976 (2017)



4. Lizhe Wang, Yan Ma, Jining Yan, Victor Chang, Albert Y. Zomaya: pipsCloud: High performance cloud computing for remote sensing big data management and processing. Future Generation Comp. Syst. 78: 353-368 (2018)



5. Lizhe Wang, Yan Ma, Albert Y. Zomaya, Rajiv Ranjan, Dan Chen: A Parallel File System with Application-Aware Data Layout Policies for Massive Remote Sensing Image Processing in Digital Earth. IEEE Trans. Parallel Distrib. Syst. 26(6): 1497-1508 (2015)















Dr. Jining Yan received his PhD in signal and information processing in the University of Chinese Academy of Sciences. He is an associate professor of School of Computer Science, China University of Geoscience. His research is focused on remote sensing data processing and information service, cloud computing in remote sensing.









Representative Publications:



1. Jining Yan, Lizhe Wang, Kim-Kwang Raymond Choo and Wei Jie. A cloud-based remote sensing data production system. Future Generation Computer Systems. 2017. http://dx.doi.org/10.1016/j.future.2017.02.044.



2. Jining Yan, Lizhe Wang. Suitability Evaluation for Products Generation from Multisource Remote Sensing Data. Remote Sensing. 2016, 8(12), 995.



3. Jining Yan, Lizhe Wang, Lajiao Chen, Lingjun Zhao, and Bomin Huang. A Dynamic Remote Sensing Data Driven Approach for Oil Spill Simulation in the Sea, Remote Sensing. 2015, 7, 7105-7125.



4. Jining Yan, Kefa Zhou, Dingsheng LiuJinlin Wang, Lizhe Wang, Hui Liu. Alteration information extraction using improved relative absorption band-depth images, from HJ_1A HSI data: a case study in Xinjiang Hatu gold ore districtInternational Journal of Remote Sensing, 2014, 35(18): 6728-6741.



5. Fan Junqing, Yan Jining*, Ma Yan, Wang Lizhe. Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure. Remote Sens. 2017, 10, 7; doi:10.3390/rs10010007.















Dr. Yan Ma is an Associate Professor at Inst. of Remote Sensing & Digital Earth, Chinese Academy of Sciences (CAS). Dr. Ma has received her M.E. and PHD degree of signal and information processing from University of Chinese Academy of Sciences. Dr. Ma also serves as an Associate Editor of Cluster Computing (Springer). She mainly lays her research interests on high performance geo-computing, parallel remote sensing image processing and Cloud Computing.









Representative Publications:











Yan Ma, Lizhe Wang, Albert Y. Zomaya, Dan Chen, Rajiv Ranjan, "Task-Tree based Large-Scale Mosaicking for Remote Sensed Imageries with Dynamic DAG Scheduling," IEEE Transactions on Parallel and Distributed Systems (TPDS), 20 Nov. 2013.









Lizhe Wang, Yan Ma*, Albert Zomaya, Rajiv Ranjan Dan Chen, "A Parallel File System with Application-aware Data Layout Policies in Digital Earth," IEEE Trans. Parallel . Syst. (TPDS), vol. 26, no. 6, pp. 1497-1508, 2015









Yan Ma, Lizhe Wang, Peng Liu, and Rajiv Ranjan. Towards building a data-intensive index for big data computing A case study of remote sensing data processing. Information Sciences, 319:171188, 2015









LZ. Wang, Y. Ma*, J. Yan, V. Chang, AY Zomaya; pipscloud: High performance cloud computingfor remote sensing big data management and processing. Future Gener. Comput.Syst.(FGCS), 78 (2018), pp. 353368









Ma Y, Wang L, Liu D, et al. Generic Parallel Programming for Massive Remote Sensing Data Processing. Cluster Computing (CLUSTER), 2012 IEEE International Conference on. IEEE, 2012: 420-428.























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