Share Email Print

Proceedings Paper

A spectral image clustering algorithm based on ant colony optimization
Author(s): Luca Ashok; David W. Messinger
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Ant Colony Optimization (ACO) is a computational method used for optimization problems. The ACO algorithm uses virtual ants to create candidate solutions that are represented by paths on a mathematical graph. We develop an algorithm using ACO that takes a multispectral image as input and outputs a cluster map denoting a cluster label for each pixel. The algorithm does this through identication of a series of one dimensional manifolds on the spectral data cloud via the ACO approach, and then associates pixels to these paths based on their spectral similarity to the paths. We apply the algorithm to multispectral imagery to divide the pixels into clusters based on their representation by a low dimensional manifold estimated by the best t ant path" through the data cloud. We present results from application of the algorithm to a multispectral Worldview-2 image and show that it produces useful cluster maps.

Paper Details

Date Published: 24 May 2012
PDF: 10 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901P (24 May 2012); doi: 10.1117/12.919082
Show Author Affiliations
Luca Ashok, Univ. of Rochester (United States)
David W. Messinger, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?