Abstract
This paper introduces an architecture of parallel remote sensing image processing software, with advantages including high scalability, platform-independence, language-independence, and so on. It helps achieve high-performance computing in this field. MPI is used as the fundamental distributed message passing protocol. An object-oriented wrapper, Boost.MPI library, is used in the software to manipulate MPI. Open Source libraries such as GDAL and Open-CV are studied in this paper to help develop detailed image processing programs and implement classification algorithms. A number of experiments are conducted to test the parallel classification programs. The results indicate that in most cases the performance is significantly improved, especially for multi-spectral remote sensing image classification, in which a highest speed-up of 3.92 is reached.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mei, A., Peng, W., Qin, Q., Liu, H.: Introduction to Remote Sensing, 1st edn. (2001)
Chen, P., Tong, Q., Guo, H.: Incentive Research of Remote Sensing Information, 1st edn. (1998)
Tong, Q., Zhang, B., Zhen, F.: Theory, Techniques and Application of Hyper-spectral Remote Sensing, 3rd edn. (2006)
Shen, Z., Luo, J., Huang, G., et al.: Distributed computing model for processing remotely sensed images based on grid computing. Information Science (2007)
Boost. MPI Document, http://www.boost.org
GDAL, 201x, GDAL-Geospatial Data Abstraction Library: Version 1.8.0, http://gdal.osgeo.org
GDAL Raster Formats, http://gdal.org/formats_list.html
Zhang, L., Zhang, L.: Hyper-spectral Remote Sensing, 1st edn. (2005)
John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995)
Bradski.G, The OpenCV Library, Dr.Dobb’s Journal of Software Tools
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Li, Z., Gao, S. (2012). Parallel Remote Sensing Image Processing: Taking Image Classification as an Example. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-34289-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34288-2
Online ISBN: 978-3-642-34289-9
eBook Packages: Computer ScienceComputer Science (R0)