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 CWEEE  Vol.9 No.3 , July 2020
Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes
Abstract: Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a naive MWSD, by making the complexity of the computational process fall from O(N2n2) to O(N2n), where N is a square input array side, and n is the moving window’s side length. The Numba python compiler was used to make python a competitive high-performance computing language in our optimizations. Our results show efficiency benchmars
Cite this paper: Conceição, M. , Mendonça, L. and Lentini, C. (2020) Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes. Computational Water, Energy, and Environmental Engineering, 9, 75-85. doi: 10.4236/cweee.2020.93006.
References

[1]   Shapiro, L.G. and Stockman, G.C. (2001) Computer Vision. Prentice Hall, Upper Saddle River.

[2]   Singha, S., Bellerby, T.J. and Trieschmann, O. (2013) Satellite Oil Spill Detection Using Artificial Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 2355-2363.
https://doi.org/10.1109/JSTARS.2013.2251864

[3]   Garcia-Pineda, O., MacDonald, I.R., Li, X., Jackson, C.R. and Pichel, W.G. (2013) Oil Spill Mapping and Measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 2517-2525.
https://doi.org/10.1109/JSTARS.2013.2244061

[4]   Awad, M. (2010) An Unsupervised Artificial Neural Network Method for Satellite Image Segmentation. The International Arab Journal of Information Technology, 7, 199-205.

[5]   Liu, Y., Zhang, M.H., Xu, P. and Guo, Z.W. (2017) SAR Ship Detection Using Sea-Land Segmentation-Based Convolutional Neural Network. 2017 IEEE International Workshop on Remote Sensing with Intelligent Processing, Shanghai, 18-21 May 2017, 1-4.
https://doi.org/10.1109/RSIP.2017.7958806

[6]   Wang, S.-H., et al. (2018) Polarimetric Synthetic Aperture Radar Image Segmentation by Convolutional Neural Network Using Graphical Processing Units. Journal of Real-Time Image Processing, 15, 631-642.
https://doi.org/10.1007/s11554-017-0717-0

[7]   Prabhu, K.M. (2013) Window Functions and Their Applications in Signal Processing. CRC Press.

[8]   Mastriani, M. and Giraldez, A.E. (2016) Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images.

[9]   Maussang, F., Chanussot, J., Hétet, A. and Amate, M. (2007) Mean-Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images. IEEE Journal of Oceanic Engineering, 32, 956-970.
https://doi.org/10.1109/JOE.2007.907936

[10]   Li, H. and Cao, J. (2010) Detection and Segmentation of Moving Objects Based on Support Vector Machine. 2010 IEEE Third International Symposium on Information Processing, Qingdao, 15-17 October 2010, 193-197.
https://doi.org/10.1109/ISIP.2010.35

[11]   Highlander, T. and Rodriguez, A. (2016) Very Efficient Training of Convolutional Neural Networks Using Fast Fourier Transform and Overlap-and-Add. In: Xie, X.H., Jones, M.W. and Tam, G.K.L., Eds., Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, Guildford, 160.1-160.9.
https://doi.org/10.5244/C.29.160

[12]   Lubin, M. and Dunning, I. (2015) Computing in Operations Research Using Julia. INFORMS Journal on Computing, 27, 238-248.
https://doi.org/10.1287/ijoc.2014.0623

[13]   Lam, S.K., Pitrou, A. and Seibert, S. (2015) Numba: A LLVM-Based Python JIT Compiler. Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, November 2015, 1-6.
https://doi.org/10.1145/2833157.2833162

[14]   So, S. (2008) Why Is the Sample Variance a Biased Estimator? Griffith University, Brisbane, Tech. Rep. 9.

[15]   Ma, Y.Z. (2019) Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling. Springer International Publishing, Berlin.
https://doi.org/10.1007/978-3-030-17860-4

[16]   Murray, M.R. and Baker, D.E. (1991) MWINDOW: An Interactive FORTRAN-77 Program for Calculating Moving-Window Statistics. Computers & Geosciences, 17, 423-430.
https://doi.org/10.1016/0098-3004(91)90049-J

[17]   Behnel, S., et al. (2011) Cython: The Best of Both Worlds. Computing in Science & Engineering, IEEE Computer Society, 13, 31-39.
https://doi.org/10.1109/MCSE.2010.118

[18]   Behnel, S., Bradshaw, R., Citro, C., Dalcin, L., Seljebotn, D.S. and Smith, K. (2011) Cython: The Best of Both Worlds. Computing in Science & Engineering, 13, 31-39.
https://doi.org/10.1109/MCSE.2010.118

[19]   Guidorizzi, H.L. (2012) Um curso de cálculo, Vol. 1, 5a edicao. Grupo Gen-LTC.

 
 
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