Cloud Computing in Remote Sensing

NOTE EDITORE

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

SOMMARIO

1 Remote Sensing and Cloud Computing 2 Remote Sensing Data Integration in a Cloud Computing Environment 3 Remote Sensing Data Organization and Management in a Cloud Computing Environment 4 High Performance Remote Sensing Data Processing in a Cloud Computing Environment 5 Programming Technologies for High Performance Remote Sensing Data Processing in a Cloud Computing Environment 6 Construction and Management of Remote Sensing Production Infrastructures across Multiple Satellite Data Centers 7 Remote Sensing Product Production in an OpenStack-based Cloud Computing Environment 8 Knowledge Discover and Information Analysis from Remote Sensing Big Data 9 Automatic Construction of Cloud Computing Infrastructures in Remote Sensing 10 Security Management in a Remote-Sensing-Oriented Cloud Computing Environment 11 A Cloud-Based Remote Sensing Information Service System Design and Implementation

AUTORE

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 Liu,Jinlin 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 district,International 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, 319:171–188, 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. 353–368 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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9781138594562
• Dimensioni: 9.25 x 6.25 in Ø 1.36 lb
• Formato: Copertina rigida
• Illustration Notes: 129 b/w images and 20 tables
• Pagine Arabe: 280
• Pagine Romane: xii