Article citationsMore>>

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

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