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A review of parallel computing for large-scale remote sensing image mosaicking

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Abstract

Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further research of image mosaicking parallelism on a large scale. This paper provides a perspective on the current state of image mosaicking parallelization for large scale applications. We firstly introduce the motivation of image mosaicking parallel for large scale application, and analyze the difficulty and problem of parallel image mosaicking at large scale such as scheduling with huge number of dependent tasks, programming with multiple-step procedure, dealing with frequent I/O operation. Then we summarize the existing studies of parallel computing in image mosaicking for large scale applications with respect to problem decomposition and parallel strategy, parallel architecture, task schedule strategy and implementation of image mosaicking parallelization. Finally, the key problems and future potential research directions for image mosaicking are addressed.

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References

  1. Moik, G.: Digital Processing of Remotely Sensed Images. NASA (1980).

  2. Jan, K., Andreas, R., Volker, C.R., Tobias, K., Jacek, K., Patrick, H.: Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Int. J. Remote. Sens., 113, 957C964 (2009)

  3. Franklin, S.E., Wulder, M.A.: Remote sensing methods in medium spatial resolution satellite data land cover classi-cation of large areas. Prog. Phys. Geog. 26, 173–205 (2002)

    Article  Google Scholar 

  4. Cihlar, J.: Land-cover mapping of large areas from satellites: status and research priorities. Int. J. Remote. Sens. 21, 1093–1114 (2000)

    Article  Google Scholar 

  5. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E.: Digital change detection methods in ecosystem monitoring: a review. Int. J. Remote. Sens. 25, 1565–1596 (2004)

    Article  Google Scholar 

  6. Hame, T., Salli, A., Andersson, K., Lohi, A.: A new methodology for the estimation of biomass of conifer dominated boreal forest using NOAA AVHRR data. Int. J. Remote. Sens. 18, 3211–3243 (1997)

    Article  Google Scholar 

  7. De Grandi, G., Malingreau, J.P., Leysen, M.: The ERS-1 Central Africa mosaic: a new perspective in radar remote sensing for the global monitoring of vegetation. IEEE T. Geosci. Remote. 37, 1730–1746 (1997)

    Article  Google Scholar 

  8. Cohen, W.B., Maiersperger, T.K., Spies, T.A., Oetter, D.R.: Modelling forest cover attributes as continuous variables in a regional context with thematic mapper data. Int. J. Remote. Sens. 22, 2279–2310 (2001)

    Article  Google Scholar 

  9. Kim, K., Jezek, K.C., Liu, H.: Orthorectified image mosaic of Antarctica from 1963 Argon satellite photography: image processing and glaciological applications. Int. J. Remote. Sens. 28, 5357–5373 (2007)

    Article  Google Scholar 

  10. Merson, R.H.: An AVHRR mosaic image of Antarctic. Int. J. Remote. Sens. 10, 669–674 (1989)

    Article  Google Scholar 

  11. Jezek, K.C.: Flow variations of the Antarctic ice sheet from comparison of modern and historical satellite data. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Seattle, vol. 4, pp. 2240–2242 (1998)

  12. Rosenqvist, A., Shimada, M., Chapman, B., Freeman, A., De Grandi, G., Saatchi, S., Rauste, Y.: The global rain forest mapping project: a review. Int. J. Remote. Sens. 21, 1375–1387 (2000)

    Article  Google Scholar 

  13. Shusun, L.: Summer environmental mapping potential of a large-scale ERS-1 SAR mosaic of the state of Alaska. Int. J. Remote. Sens. 20, 387–401 (1999)

    Article  Google Scholar 

  14. Homer, C.G., Ramsey, R.D., Edwards, T.C., Falconer, A.: Landscape cover-type modeling using a multi-scene thematic mapper mosaic. Photogramm. Eng. Rem. S. 63, 59–67 (1997)

    Google Scholar 

  15. Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E.: Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sens. Environ. 112, 955–969 (2008)

    Article  Google Scholar 

  16. Matthew, C.H., Thomas, R.L.: A review of large area monitoring of land cover change using landsat data. Remote. Sens. Environ. 122, 66–74 (2012)

    Article  Google Scholar 

  17. Muller, S.V., Racoviteanu, A.E., Walker, D.A.: Landsat MSS-derived land-cover map of northern Alaska: extrapolation methods and a comparison with photo-interpreted and AVHRR-derived maps. Int. J. Remote. Sens. 20, 2921–2946 (1999)

    Article  Google Scholar 

  18. Shimabukuro, Y.E., Novo, E.M., Merte, L.K.: Amazon River mainstem flood landsat TM digital mosaic. Int. J. Remote. Sens. 23, 57–69 (2002)

    Article  Google Scholar 

  19. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.K.: A landsat surface reflectance dataset for North America, 1990–2000. IEEE T. Geosci. Remote. 3, 68–72 (2006)

    Article  Google Scholar 

  20. Homer, C., Huang, C.Q., Yang, L.M., Wylie, B., Coan, M.: Development of a 2001 national land-cover database for the United States. Photogramm. Eng. Rem. S. 70, 829–840 (2004)

    Article  Google Scholar 

  21. Vogelmann, J.E., Howard, S.M., Yang, L.M., Larson, C.R., Wylie, B.K., Van Driel, N.: Completion of the 1990s national land cover data set for the conterminous United States from landsat thematic mapper data and ancillary data sources. Photogramm. Eng. Rem. S. 67, 650–662 (2004)

    Google Scholar 

  22. David, P.R., Junchang, J., Kristi, K., Pasquale, L.S., Valeriy, K., Matthew, H., Thomas, R.L., Eric, V., Chunsun, Z.: Web-enabled landsat data (WELD): landsat ETM+ composited mosaics of the conterminous United States. Remote. Sens. Environ. 114, 35–49 (2010)

    Article  Google Scholar 

  23. Li, Z., Nadon, S., Cihlar, J.: Satellite-based detection of Canadian boreal forest fires: development and application of the algorithm. Int. J. Remote. Sens. 21, 3057–3069 (2000)

    Article  Google Scholar 

  24. Wei, Z., Chen, Z.C., Zhang, B.: CBERS-1 digital images mosaic and mapping of China. J. Imag. Graph. 11, 787–791 (2006)

    Google Scholar 

  25. Wang, A., Chi, Y., Wang, Z., Wu, F., Wang, X., Li, L., Yan, M.: Study on mosaic technology and mapping of China based on the multispectral images of Beijing-1 small satellite. J. Remote Sens. 13, 83–90 (2009)

    MATH  Google Scholar 

  26. Bindschadler, R., Brownworth, F., Stephensonn, S.: Landsat thematic mapper imagery of the Siple coast. Antarct. Antarct. J. U. S. 23, 214–215 (1988)

    Google Scholar 

  27. Bennat, H., Sievers, J.: Remote sensing and GIS application in Antarctica. EARSel Adv. Remote Sens. 1, 160–168 (1992)

    Google Scholar 

  28. Ferrigno, J.G., Mullins, J.L., Stapleton, J.A., Chavez, P.S., Velasco, M.G., Williams, R.S.: Satellite image map of Antarctica, miscellaneous investigations map series. US Geol. Surv. 1–2560 (1996)

  29. USGS, Satellite Image Map of Antarctica, 1:5, 000, 000, Miscellaneous Map Investigation Series, I-2284 (1991).

  30. Jezek, K.: Glaciological properties of the Antarctic ice sheet, from Radarsat-1 synthetic aperture radar imagery. Ann. Glaciol. 29, 286–290 (1999)

    Article  Google Scholar 

  31. Scambos, T., Haran, T., Fahnestock, M., Painter, T.H., Bohlander, J.: MODIS-based mosaic of Antarctica (MOA) data sets: continent-wide surface morphology and snow grain size. Remote Sens. Environ. 111, 242–257 (2007)

    Article  Google Scholar 

  32. Bindschadler, R., Vornberger, P., Fleming, A., Fox, A., Mullins, J., Binnie, D., Paulsen, S., Sara, J., Granneman, B., Gorodetzky, D.: The landsat image mosaic of Antarctica. Remote Sens. Environ. 112, 4214–4226 (2008)

    Article  Google Scholar 

  33. Turner, P.J., Prata, A.J., Howden, R.T., Houghton and Taylor, N.R.: An ATSR-2 mosaic image of Australia. Int. J. Remote. Sens. 22, 3889–3894 (2001)

    Article  Google Scholar 

  34. Rauste, Y.: Compilation of bi-temporal JERS-1 SAR mosaics over the African rain forest belt in the GRFM project. In: Proceedings of the Presented at the International Geoscience and Remote Sensing Symposium (IGARSS99), Hamburg, 1999, 28 June–2 July 1999 (IEEE Publications), vol. 2, pp. 750–752 (1999)

  35. Khatib, L., Gasch, J., Morris, R., Covington, S.: Local search for optimal global map generation using mid-decadal landsat images. In: Proceedings of AAAI Workshop on Preference Handling for Arti-cial Intelligence, Vancouver (2007)

  36. Mayaux, P., Achard, F., Malingreau, J.: Global tropical forest area measurements derived from coarse resolution satellite imagery: a comparison with other approaches. Environ. Conserv. 25, 37–52 (1998)

    Article  Google Scholar 

  37. Tucker, C.J., Denelle, M.G., Dykstm, D.: NASA’s global orthorectified landsat data set. Photogramm. Eng. Rem. S. 70, 313–322 (2004)

    Article  Google Scholar 

  38. Lee, C.A., Gasster, S.D., Plaza, A., et al.: Recent developments in high performance computing for remote sensing: a review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4(3), 508–527 (2011)

    Article  Google Scholar 

  39. Dongarra, J., Sterling, T., Simon, H., et al.: High-performance computing: clusters, constellations, MPPs, and future directions. Comput. Sci. Eng. 7(2), 51–59 (2005)

    Article  Google Scholar 

  40. Hu, B., Zhou, H., Wang, P., Liu, H.: A parallel algorithm of PCA image fusion in remote sensing and its implementation. Microelectron. Comput. 23, 153–155 (2006)

    Google Scholar 

  41. Plaza, A., Valencia, D., Plaza, J., Martinez, P.: Commodity cluster based parallel processing of hyperspectral imagery. J. Parallel Distrib. Comput. 66, 345C358 (2006)

    Article  Google Scholar 

  42. An, X., Wang, X., Du, Z., Liu, D., Li, G.: Fine-grained parallel algorithm for remote sensing image mosaics for cluster system. J. Tsinghua Univ. (Sci. and Tech.), 42, 1389–1392 (2002)

  43. Hu, B., Liu, H.Z., Wang, P.F., et al.: Research on parallel image fusion model on pixel scale. Comput. Era 2, 6–8 (2008)

    Google Scholar 

  44. Chen, C., Tan, Y.H., Li, H.T., Gu, H.Y.: A fast and automatic parallel algorithm of remote sensing image mosaic. Microelectron. Comput. 28, 59–62 (2011)

    Google Scholar 

  45. Chen, C.: High Performance Image Mosaicking for HJ-1 Satellites Images. Huazhong University of Science and Technology, Wuhan (2011)

    Google Scholar 

  46. Wang, Y., Ma, Y., Liu, P., Liu, D., Xie, J.: An optimized image mosaic algorithm with parallel i/o and dynamic grouped parallel strategy based on minimal spanning tree. In: Proceedings of the Ninth International Conference on Grid and Cloud Computing, Beijing (2010)

  47. Ma, Y., Wang, L., Zomaya, A., Chen, D., Ranjan, R.: Task-tree based large-scale mosaicking for massive remote sensed imageries with dynamic DAG scheduling. IEEE Trans. Parallel Distrib. Syst. 25(8), 2126–2137 (2014)

  48. Yang, Y.: Design and implementation of high performance mosaic system for remote sensing image, MS Thesis, Huazhong University of Science and Technology, Wuhan (2012)

  49. Merzky, A., Stamou, K., Jha, S., Katz, D.S.: A fresh perspective on developing and executing DAG-based distributed applications: a case-study of SAGA-based montage. In: Proceedings of the 5th IEEE International Conference on e-Science, pp.231–238 (2009)

  50. Berriman, G.B., Good, J.C., Curkendall, D., Jacob, J., Katz, D.S., Prince, T.A., Williams, R.: Montage: an on-demand image mosaic service for the NVO. In: Proceedings of the Astronomical Data Analysis Software and Systems (ADASS) XII, (2002)

  51. Rabenseifner, R., Hager, G., Jost, G.: HybridMPI/OpenMP parallel programming on clusters of multi-core SMP nodes. In: Proceedings of the Parallel, Distributed and Network-based Processing (PDP), pp. 427–436 (2009)

  52. Wang, H.: Parallel Algorithms for Image and Video Mosaic Based Applications, MS Thesis, University of Georgia, Atlanta (2005)

  53. Nickolls, J., Dally, W.J.: The GPU computing era. IEEE Micro 30, 56C69 (2010)

    Article  Google Scholar 

  54. Balz, T., Stilla, U.: Hybrid GPU-based single and double bounce SAR simulation. IEEE Trans. Geosci. Remote Sens. 47, 3519C3529 (2009)

    Article  Google Scholar 

  55. Qin, C.Z., Zhan, L.J.: Parallelizing flow-accumulation calculations on graphics processing unitsFrom iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm. Comput. Geosci. 43, 7–16 (2012)

    Article  Google Scholar 

  56. Camargo, A., Schultz, R.R., Wang, Y., Fevig, R.A., He, Q.: GPU-CPU implementation for super-resolution mosaicking of unmanned aircraft system (UAS) surveillance video. In: Proceedings IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 25–28 (2010)

  57. Yong, K.A.T., Wee, J.T., Leong, K.K.: Fast colour balance adjustment of IKONOS imagery using CUDA. In: Proceedings of the Geoscience and Remote Sensing Symposium, pp. 1052–1055 (2008)

  58. Qin, C.Z., Zhan, L.J., Zhu, A.X., Zhou, C.H.: A strategy for raster-based geocomputation under different parallel computing platforms. Int. J. Geogr. Inform. Sci. doi:10.1080/13658816.2014.911300 (2014)

  59. Thenkabail, P.S.: Characterization of the alternative to slash-and-burn benchmark research area representing the Congolese rainforests of Africa using near-real-time SPOT HRV data. Int. J. Remote. Sens. 20, 839–877 (1999)

    Article  Google Scholar 

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Acknowledgments

This study is supported by National Natural Science Foundation (41301028), Project of State Key Laboratory of Resources and Environmental Information System (LREIS).

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Authors and Affiliations

  1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 10094, China

    Lajiao Chen, Yan Ma & Peng Liu

  2. Nanchang University, Nanchang, 330031, China

    Jingbo Wei

  3. University of West London, W5 5RF, London, UK

    Wei Jie

  4. Base of the State Laboratory of Urban Environmental Processes and Digital Modeling, College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China

    Jijun He

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  1. Lajiao Chen
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  2. Yan Ma
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  3. Peng Liu
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Correspondence to Peng Liu.

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Chen, L., Ma, Y., Liu, P. et al. A review of parallel computing for large-scale remote sensing image mosaicking. Cluster Comput 18, 517–529 (2015). https://doi.org/10.1007/s10586-015-0422-3

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