.png){kind=icon}
Return
An SMP soft classification algorithm for remote sensing
.png){kind=icon}
OA
AI
DOI：10.1016/j.cageo.2014.03.010.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification 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 classification is generated in just over 4 min using 32 processors. (C) 2014 Published by Elsevier Ltd.
Keywords:
Remote sensing
Semisupervised clustering
Classification
IGSCR
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.4
Papers:
5.0K
Citations:
1.5W
Organization
No organization information available