디지털융복합연구 (Journal of Digital Convergence)

  • 제13권2호
  • /
  • Pages.193-204
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  • 2015
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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Array DBMS을 이용한 위성원격탐사 영상의 3차원 시각화

3D Visualization of Satellite Remote-Sensing Images Using an Array DBMS

  • 최종혁 (충북대학교 컴퓨터교육과) ;
  • 이종연 (충북대학교 소프트웨어학과)
  • Choi, Jong Hyeok (Dept. of Computer Education, Chungbuk National University) ;
  • Lee, Jong Yun (Dept. of Software Engineering, Chungbuk National University)
  • 투고 : 2014.12.20
  • 심사 : 2015.02.20
  • 발행 : 2015.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

배열형 DBMS는 배열형 자료의 저장 및 분석이 가능하므로 과학자들로부터 많은 기대를 받고 있다. 본 논문은 배열형 DBMS를 이용하여 위성원격탐사 영상을 처리하는 방법의 기술이다. 하지만 기존의 연구 방법들은 크게 두 가지 문제가 있다. 첫째, 지구곡률 등에 의해 왜곡된 데이터가 그대로 시각화되는 문제를 가진다. 둘째, 기 작성된 질의를 통해 얻어진 시각화 결과를 다른 분석에 활용하기 어렵다. 따라서 본 논문은 이런 문제점을 해소하고 더 나아가 기존의 2차원 시각화가 아닌 위성원격탐사 영상의 3차원 시각화 방법을 제안한다. 아울러 논문의 세부적인 연구내용은 다음과 같다. 첫째, 위성원격탐사 영상을 배열 데이터베이스에 저장, 분석, 가공하는 방법을 기술한다. 둘째, 가공된 결과물의 3차원 시각화 방법을 제안한다. 마지막으로 본 논문의 학술적 기여도는 배열형 DBMS에서의 위성원격탐사 영상의 활용 방법과 함께 위성데이터의 3차원 시각화 기법을 제안했고, 위성원격탐사 영상의 활용범위 확대에 기여할 것으로 요약된다.

An array DBMS has been expected widely from scientists because it is convenient to store and analyze the data of array type. In this paper, we describe how to handle satellite remote-sensing images in the array DBMS. However, previous works in their visualization have two problems as follows. First, the images are visualized as a state of distorted by the curvature of the earth. Second, it is difficult to apply the results of visualization by pre-written queries to other analyses. Therefore, this paper proposes a three dimensional visualization method of satellite remote-sensing images, not traditional 2D visualization. Our research contents are as follows. First, we describe how to store, process, and analyze the satellite remote-sensing images in the array DBMS. Second, we propose a three-dimensional visualization method for their processed outputs. Lastly, our contributions can be summarized that we propose a method of handling satellite remote-sensing images in the array DBMS and their 3D visualization techniques. It is also expected that their use be available widely in many industrial areas.

키워드

참고문헌

  1. Lavanya Ramakrishnan, Pradeep K. Mantha, Yushu Yao, Richard S. Canon, Evaluation of NoSQL and Array Databases for Scientific Applications, DataCloud Workshop, 2013.
  2. M Stonebraker, P Brown, A Poliakov, S Raman, The Architecture of SciDB, Scientific and statistical database management, pp.1-16, 2011.
  3. Bill Howe (2007). GRIDFIELDS: Model-Driven Data Transformation in the Physical Sciences. Ph.D. dissertation, p.276, Portland State University.
  4. Peter Baumann, Paula Furtado, Roland Ritsch, Norbert Widmann, The RasDaMan approach to multidimensional database management, 1997 ACM symposium on Applied computing, pp.166-173, 1997.
  5. Qingyun Xie, Siva Ravada, Weisheng Xu, Zhihai Zhang, An enterprise database-centric approach for geospatial image management and processing, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 37, pp.199-204, 2008.
  6. Peter Alexander Boncz, Monet: a next-Generation DBMS Kernel for Query-Intensive Applications, Ph.D. dissertation, p.209, Universiteit van Amsterdam, 2002.
  7. Regina O. Obe, Leo S. Hsu, PostGIS in Action, p.520, Manning publications, 2011
  8. Gary Planthaber, Michael Stonebraker, James Frew, EarthDB: scalable analysis of MODIS data using SciDB, BigSpatial'12, pp.11-19, 2012
  9. Michael Stonebraker, Jennie Duggan, Leilani Battle, Olga Papaemmanouil, SciDB DBMS Research at MIT, IEEE Data Engineering Bull, Vol. 36, No. 4, pp.21-30, 2013.
  10. Michael Stonebraker, Jacek Becla, David Dewitt, Kian-Tat Lim, David Maier, Oliver Ratzesberger, Stan Zdonik, Requirements for Science Data Bases and SciDB, CIDR, Vol. 7, pp.173-184, 2009.
  11. P.G. Brown, Overview of SciDB: large scale array storage, processing and analysis, 2010 ACM SIGMOD International Conference on Management of data, pp.963-968, 2010.
  12. P. Cudre-Mauroux, H. Kimura, K.-T. Lim, J. Rogers, R. Simakov, E. Soroush, P. Velikhov, D. L. Wang, M. Balazinska, J. Becla, D. DeWitt, B. Heath, D. Maier, S. Madden, J. Patel, M. Stonebraker, S. Zdonik, A demonstration of SciDB: a science-oriented DBMS, VLDB Endowment, Vol. 2, No. 2, pp1534-1537, 2009. https://doi.org/10.14778/1687553.1687584
  13. Michael Stonebraker, Paul Brown, Donghui Zhang, Jacek Becla, SciDB: A database management system for applications with complex analytics, Computing in Science & Engineering, Vol. 15, No. 3, pp.54-62, 2013. https://doi.org/10.1109/MCSE.2013.19
  14. Global 30 Arc-Second Elevation (GTOPO30), https://lta.cr.usgs.gov/GTOPO30, January 24, 2012.
  15. Philippe Cudre-Mauroux, Hideaki Kimura, Kian-Tat Lim, Jennie Rogers, Samuel Madden, Michael Stonebraker, Stanley B. Zdonik,Paul G. Brown, SS-DB: a standard science DBMS benchmark, 2012 XLDB, 2012.
  16. Emad Soroush, Magdalena Balazinska, Daniel Wang, Arraystore: a storage manager for complex parallel array processing, 2011 ACM SIGMOD International Conference on Management of data, pp.253-264, 2011.
  17. Leilani Battle, Michael Stonebraker, Remco Chang, Dynamic reduction of query result sets for interactive visualizaton, 2013 IEEE International Conference on Big Data, pp.1-8, 2013.
  18. Emad Soroush, Magdalena Balazinska, Hybrid merge/overlap execution technique for parallel array processing, EDBT/ICDT 2011 Workshop on Array Databases, pp.20-30, 2011.
  19. George Riggs, Dorothy K.Hall, Snow mapping with the MODIS Aqua instrument, 61st Eastern snow conference, Vol. 9, No. 11, pp.81-84, 2004.
  20. Emad Soroush, Magdalena Balazinska, Time travel in a scientific array database, 2013 IEEE International Conference on Data Engineering, pp.98-109, 2013.