An SMP soft classification algorithm for remote sensing

Authors:
Rhonda D. Phillips;Layne T. Watson;Randolph H. Wynne
Affiliations:
MIT Lincoln Laboratory, Lexington, MA;Virginia Polytechnic Institute and State University Blacksburg, VA;Virginia Polytechnic Institute and State University Blacksburg, VA
Venue:
Proceedings of the 19th High Performance Computing Symposia
Year:
2011

Citing 6
Cited 0

D-ISODATA: a distributed algorithm for unsupervised classification of remotely sensed data on network of workstations

Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Remote Sensing Digital Image Analysis: An Introduction

Remote Sensing Digital Image Analysis: An Introduction
Parallel Fuzzy c-Means Clustering for Large Data Sets

Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
A Data-Clustering Algorithm on Distributed Memory Multiprocessors

Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Hybrid image classification and parameter selection using a shared memory parallel algorithm

Computers & Geosciences
Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China

International Journal of Remote Sensing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR), a semi-automated 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 108 pixels and 6 bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors.