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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

has been cited by the following article:

• TITLE: Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

  AUTHORS: Marcos R. de A. Conceição, Luis F. F. de Mendonça, Carlos A. D. Lentini

  KEYWORDS: Digital Image Processing, Image Segmentation, Standard Deviation, Python, Machine Learning

  JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.9 No.3, July 30, 2020

  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