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