Korean Journal of Remote Sensing (대한원격탐사학회지)

The Korean Society of Remote Sensing (대한원격탐사학회)
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Evaluation of Geo-based Image Fusion on Mobile Cloud Environment using Histogram Similarity Analysis

  • Lee, Kiwon (Department of Information Systems Engineering, Hansung University) ;
  • Kang, Sanggoo (Department of Information Systems Engineering, Hansung University)
  • Received : 2014.11.23
  • Accepted : 2014.12.29
  • Published : 2015.02.28
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Abstract

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

Keywords

Acknowledgement

Grant : 멀티플랫폼 레이더 원격탐사를 이용한 재해 모니터링 글로컬 기술 개발

Supported by : 서울시립대학교

References

  1. Barnatt, C. 2010. A brief guide to cloud computing, pp. 1-289 (UK: Constable & Robinson Ltd.).
  2. Bogaert, P. and D. Fasbender, 2007. Bayesian data fusion in a spatial prediction context: a general formulation, Stochastic Environmental Research and Risk Assessment, 21(6): 695-709. https://doi.org/10.1007/s00477-006-0080-3
  3. Bradski, G. and A. Kaehler, 2008. Learning OpenCV computer vision with OpenCV library, pp. 199-206 (USA: O'Reilly).
  4. Evangelidis, K., K. Ntouros, S. Makridis, and C. Papatheodorou, 2014. Geospatial services in the cloud, Computers and Geosciences, 63:116-122. https://doi.org/10.1016/j.cageo.2013.10.007
  5. Fasbender, D., V. Obsomer, P. Bogaert, and P. Defourny, 2009. Updating scarce high resolution images with time series of coarser images: a bayesian data fusion solution, In Sensors and Data Fusion, N. Milisavljevic (Ed.), pp. 245-262 (Vienna: I-Tech Education and Publishing).
  6. Fernando, N., S.W. Loke, and W. Rahayu, 2013. Mobile cloud computing: a survey, Future Generation Computer Systems, 29: 84-106. https://doi.org/10.1016/j.future.2012.05.023
  7. Gasperi, J., 2013. Cloud computing & web processing services, Available online at: https://speakerdeck.com/jjrom/cloud-computing-andweb-processing-services (accessed May 2014).
  8. Hung, S.-H., C.-S. Shih, J.-P. Shieh, C.-P. Lee, and Y.- H. Huang, 2012. Executing mobile applications on the cloud: framework and issues, Computers and Mathematics with Applications, 63(2): 573-587. https://doi.org/10.1016/j.camwa.2011.10.044
  9. Jackson, K. and C. Bunch, 2013. OpenStack cloud computing cookbook, 2nd Edition, pp. 1-396 (Birmingham-Mumbai: Packt Publishing).
  10. Jeong, U., D. Kang, and S. Jung, 2011. Trend of open source SW-based cloud computing technology, Electronics and Telecommunications Trends, 26: 43-54 (in Korean).
  11. 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). https://doi.org/10.7780/kjrs.2012.28.4.10
  12. Kang, S. and K. Lee, 2013a. Mobile app approach by open source stack for satellite images utilization, Remote Sensing Letters, 4(4): 648-656. https://doi.org/10.1080/2150704X.2013.781286
  13. Kang, S. and K. Lee, 2013b. Testing implementation of remote sensing image analysis processing service on OpenStack of open source cloud platform, Journal of the Korean Association of Geographic Information Studies, 16(4): 141-152 (in Korean with English abstract). https://doi.org/10.11108/kagis.2013.16.4.141
  14. Lee, K. and S. Kang, 2013. Mobile cloud service of geo-based image processing functions: a test iPad implementation, Remote Sensing Letters, 4(9): 910-919. https://doi.org/10.1080/2150704X.2013.810821
  15. Ma, Y., X. Gu, and Y. Wang, 2010. Histogram similarity measure using variable bin size distance, Computer Vision and Image Understanding, 114(8): 981-989. https://doi.org/10.1016/j.cviu.2010.03.006
  16. OpenCV, 2014. OpenCV 2.4.9.0 documentation, Available online at: http://docs.opencv.org/doc/tutorials/ imgproc/histograms/histogram_comparison/histogram_comparison.html (accessed April 2014).
  17. OpenStack, 2014. OpenStack Installation Guide for Ubuntu 12.04 (LTS) Havana, Available online:http://docs.openstack.org/havana/installguide/install/apt/content/index.html (accessed August 2014)
  18. OTB Development Team, 2014. the ORFEO Tool box software guide updated for OTB-4.0, Available online at:http://www.orfeo-toolbox.org/packages/OTBSoftwareGuide.pdf (accessed August 2014).
  19. Sanaei, Z., S. Abolfazli, A. Gani, and R. Buyya, 2014. Heterogeneity in mobile cloud computing: taxonomy and open challenges, IEEE Communications Surveys & Tutorials, 16(1):369-392. https://doi.org/10.1109/SURV.2013.050113.00090
  20. Tang, W. and W. Feng, 2014. Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphicsprocessing units, Computers, Environment and Urban Systems, http://dx.doi.org/10.1016/j.compenvurbsys.2014.01.001
  21. Yang, C., M. Goodchild, Q. Huang, D. Nebert, R. Raskin, Y. Xu, M. Bambacus, and D. Fay, 2011. Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?, International Journal of Digital Earth, 4(4): 305-329. https://doi.org/10.1080/17538947.2011.587547
  22. Yue, P., H. Zhou, J. Gong, and L. Hu, 2013. Geoprocessing in cloud computing platforms - a comparative analysis, International Journal of Digital Earth, 6(4): 404-425. https://doi.org/10.1080/17538947.2012.748847
  23. Zhang, J. 2010. Multi-source remote sensing data fusion: status and trends, International Journal of Image and Data Fusion, 1(1): 5-24. https://doi.org/10.1080/19479830903561035

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