Cloud-based Satellite Image Processing Service by Open Source Stack: A KARI Case |
Lee, Kiwon
(Department of Electronics and Information Engineering, Hansung University)
Kang, Sanggoo (Department of Electronics and Information Engineering, Hansung University) Kim, Kwangseob (Department of Electronics and Information Engineering, Hansung University) Chae, Tae-Byeong (Korea Aerospace Research Institute (KARI)) |
1 | Barnatt, C., 2010. A Brief Guide to Cloud Computing. Constable & Robinson Ltd., London, United Kingdom. |
2 | Evangelidis, K., K. Ntouros, S. Makridis, and C. Papatheodorou, 2014. Geospatial Services in the Cloud, Computers and Geosciences, 63(2): 116-122. DOI |
3 | Guo, W., J. Ya, G. Wan, S. Jiang, Y. Liu, and B. She, 2010. OpenRS-Cloud: A remote sensing image processing platform based on cloud computing environment, Science China Technological Sciences, 53(5): 221-230. DOI |
4 | Kang, S. and K. Lee, 2013. Testing Implementation of Remote Sensing Image Analysis Processing Service on OpenStack of Open Source Cloud Platform, Journal of the Korean Association Geographic Information Studies, 16(4): 141- 152 (in Korean with English abstract). DOI |
5 | Kang, S. and K. Lee, 2014. Performance Test of Mobile Cloud Service by Bayesian Image Fusion, Korean Journal of Remote Sensing, 30(4): 445-454 (in Korean with English abstract). DOI |
6 | Kang, S. and K. Lee, 2016. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment, Remote Sensing, 8(8): 1-10. |
7 | Kim, I.-H. and M.-H. Tsou, 2013. Enabling Digital Earth simulation models using cloud computing or grid computing; two approaches supporting high-performance GIS simulation frameworks, International Journal of Digital Earth, 6(4): 383-403. DOI |
8 | Lee, K., 2012. Open Source Cloud Computing: An Experience Case of Geo-based Image Handling in Amazon Web Services, Korean Journal of Remote Sensing, 28(3): 337-346. DOI |
9 | Lee, K. and S. Kang, 2013. Mobile cloud service of geo-based image processing functions: a test iPad implementation, Remote Sensing Letter, 4(9): 910-919. DOI |
10 | Patterson, M. T., N. Anderson, C. Bennett, J. Bruggemann, R. Grossman, M. Handy, V. Ly, D. Mandl, S. Pederson, J. Pivarski, R. Powell, J. Spring, and W. Wells, 2016. The Matsu Wheel: A Cloud-based Framework for the Efficient Analysis and Reanalysis of Earth Satellite Imagery, 2016 IEEE Second Internatinal Coference on Multimedia Big Data (BigMM), Taipei, Taiwan, Apr. 20-22, pp. 156-165. |
11 | Yang C. and C. Xu, 2013. Spatial Cloud Computing: A Practical Approach, CRC Press, Boca Raton, Florida, USA. |
12 | Yoon, G. and K. Lee, 2015. WPS-based Satellite Image Processing on Web Framework and Cloud Computing Environment, Korean Journal of Remote Sensing, 31(6): 561-570 (in Korean with English abstract). DOI |
13 | OTB Development Team, 2015. The ORFEO Tool Box Software Guide Updated for OTB-4.4, CNES, Paris, France, p. 784. |
14 | Huang, Q., C. Yang, K. Benedict, S. Chen, A. Rezgui, and J. Xie, 2013. Utilize cloud computing to support dust storm forecasting, International Journal of Digital Earth, 6(4): 338-355. DOI |
15 | Kang, S., K. Lee, and Y. Kim, 2012. Preliminary performance testing of geo-spatial image parallel processing in the mobile cloud computing service, Korean Journal of Remote Sensing, 28(4): 467-475 (in Korean with English abstract). DOI |
16 | Lee, K. and S. Kang, 2015. Evaluation of Geo-based Image Fusion on Mobile Cloud Environment using Histogram Similarity Analysis, Korean Journal of Remote Sensing, 31(1): 1-9. DOI |
17 | Mell, P. and T. Grance, 2011. The NIST Definition of Cloud Computing, NIST Special Publication 800-145, United States. |
18 | OpenStack, 2017. OpenStack Documentation, Rackspace Cloud Computing, United States. |