Article

Article title IMAGE PARALLEL PROCESSING SUBSYSTEM FOR MONITORING FOREST LAND
Authors A.A. Doudkin
Section SECTION II. MATHEMATICAL AND SOFTWARE OF SUPERCOMPUTERS
Month, Year 12, 2014 @en
Index UDC 004.93’1; 004.932
DOI
Abstract This paper describes the parallel implementation of image processing algorithms in a sub-system for forest land monitoring based on remote sensing data. A multi agent approach is proposed to use for parallelization. The computational process is represented as a directed acyclic graph. Vertices of the graph correspond to the processing operations and the arcs define the order of operations. The graph is formed for each data type, then a generalized graph is constructed as a composition of the original graphs of specific scheme processing (scenarios). Such representation of computational process provides an opportunity to apply for the optimization calculations of the theory of graphs and network theory both for static (off-line scheduling) and for dynamic optimization (on-line scheduling). An integrated set of tools is proposed for the design of parallel algorithms and parallel computing, including visual editor, compiler, schedule optimization system and a system of parallel computing based on MPI.

Download PDF

Keywords Forest monitoring; remote sensing; parallel processing; multi agent system; MPI.
References 1. Belyaev B.I., Katkovskiy L.V. Opticheskoe distantsionnoe zondirovanie [Optical remote sensing]. Minsk: BGU, 2006, 455 p.
2. Monitoring biologicheskogo raznoobraziya lesov Rossii: metodologiya i metody [Monitoring of the biological diversity of forests in Russia: methodology and techniques], Ed. A.S. Isaeva.
CEPF RAS. Moscow: Nauka, 2008, 453 p.
3. Grigor'eva O.V. Nablyudenie degradatsii lesov po dannym giperspektral'nogo aero- i kosmicheskogo zondirovaniya [Observation of forest degradation according hyperspectral aerial and satellite sensing], Issledovanie Zemli iz kosmosa [Study of Earth from space], 2013, No. 1, pp. 43-47.
4. Markov A.V. i dr. Avtomatizirovannye metody otsenki sostoyaniya okruzhayushchey sredy po dannym mul'ti- i giperspektral'noy s"emki [Automated methods for environmental assessment according to multi- and hyperspectral survey], Geomatika [Geomatics], 2012, No. 4, pp. 102-106.
5. Argiro D. et al. Khoros: An integrated development environment for scientific computing and visualization, Khoral Research, Inc. Available at: https://www.cs.purdue.edu/homes/enh/PSEbook/PARTII/ papersii/khoros.pdf (Accessed 17 October 2014).
6. Wickel J. et al. Axiom - a modular visual object retrieval system / M. Jarke, J. Koehler, G. Lakemeyer, editors, KI 2002: Advances in Artificial Intelligence. LNAI 2479. Springer, 2002, pp. 253-267.
7. Dean J., Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters. Available at: http://research.google.com/ archive/mapreduce.html (Accessed 17 October 2014).
8. MPI: A Message-Passing Interface Standard Version 3.0. Message Passing Interface Forum, September 21, 2012. Available at: http://www.mpi-forum.org/docs/mpi-3.0/mpi30-report.pdf (Accessed 17 October 2014).
9. Otwagin A. Multiagent System for Reliable and Efficient Parallel Computing // Proc. of the 5th International Conf. on Neural Networks and Artificial Intelligence (ICNNAI’2008) (Minsk, 27–30 May, 2008). Minsk, 2008, pp. 46-50.
10. Sadykhov R.Kh., Ganchenko V.V., Podenok L.P. Fuzzy clustering methods in multispectral satellite image segmentation, International Journal of Computing, 2009, Vol. 8 (1), pp. 87-94.
11. Ganchenko V.V. i dr. Obrabotka dannykh distantsionnogo zondirovaniya zemli dlya zadach zemlepol'zovaniya i monitoringa zemel'nykh resursov [Processing of Remote Sensing Data for the problems of land use and land resources monitoring], Sistemy nablyudeniya, monitoringa i distantsionnogo zondirovaniya Zemli (Sistemy D33'2010): tr. VII nauch.-tekhn. konf. (Sochi, Adlerskiy r-n, pos. Veseloe, 13–17 sent. 2010 g.) [Observation systems, monitoring, and remote sensing (Systems of RSD'2010): proc. of the VII scientific-tech. conf. (Sochi, Veseloe, 13-17 September, 2010)]. Moscow, 2010, pp. 181-186.
12. Beaumont O., Legrand A., Robert Y. Static scheduling strategies for heterogeneous systems, Computing and Informatics, 2002, No. 21, pp. 413-430.
13. Hagras T., Janecek J.A. Fast Compile-Time Task Scheduling Heuristic for Homogeneous Computing Environments, International Journal of Computers and Their Applications, 2005, No. 12 (2), pp. 76-82.
14. Anishchenko V.V. i dr. Printsipy sozdaniya bazovykh konfiguratsiy superkomp'yuternykh sistem otraslevogo naznacheniya [Principles for creating basic configurations supercomputer
systems branch for destination], Informatika [Informatics], 2012, No. 1 (33), pp. 97-105.
15. Hwang К., Xu Z. Scalable Parallel Computing Technology, Architecture, Programming. USA: McGraw-Hill, 1998, 832 p.
16. Ganchenko V.V., Dudkin A.A. Reshenie zadach lesopol'zovaniya i monitoringa zemel'nykh resursov na osnove obrabotki dannykh distantsionnogo zondirovaniya Zemli [Problem solving forest management and monitoring of land resources based on the processing of remote sensing data], Tezisy dokl. Vtoroy Mezhdunar. nauch.-tekhn. konf. «Aktual'nye problemy sozdaniya kosmicheskikh sistem distantsionnogo zondirovaniya Zemli» (Moskva, 16 maya 2014 g.) [Abstracts of the Second International Scientific Conference «Actual problems of creation of space systems for remote sensing of the Earth» (Moscow, 16 May 2014)]. Moscow: OAO «Korporatsiya «VNIIEM», 2014, pp. 11-13.

Comments are closed.