An SMP Soft Classification Algorithm for Remote Sensing

Abstract

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classiï¬cation algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classiï¬cation containing inherently more information than a comparable hard classiï¬cation at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classiï¬cation is generated in just over four minutes using 32 processors