
Proceedings Paper
Assessing the impact of background spectral graph construction techniques on the topological anomaly detection algorithmFormat | Member Price | Non-Member Price |
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Paper Abstract
Anomaly detection algorithms have historically been applied to hyperspectral imagery in order to identify pixels
whose material content is incongruous with the background material in the scene. Typically, the application
involves extracting man-made objects from natural and agricultural surroundings. A large challenge in designing
these algorithms is determining which pixels initially constitute the background material within an image. The
topological anomaly detection (TAD) algorithm constructs a graph theory-based, fully non-parametric topological
model of the background in the image scene, and uses codensity to measure deviation from this background. In
TAD, the initial graph theory structure of the image data is created by connecting an edge between any two
pixel vertices x and y if the Euclidean distance between them is less than some resolution r. While this type of
proximity graph is among the most well-known approaches to building a geometric graph based on a given set of
data, there is a wide variety of dierent geometrically-based techniques. In this paper, we present a comparative
test of the performance of TAD across four dierent constructs of the initial graph: mutual k-nearest neighbor
graph, sigma-local graph for two different values of σ > 1, and the proximity graph originally implemented in
TAD.
Paper Details
Date Published: 24 May 2012
PDF: 11 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901Z (24 May 2012); doi: 10.1117/12.918889
Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 11 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901Z (24 May 2012); doi: 10.1117/12.918889
Show Author Affiliations
Amanda K. Ziemann, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
James A. Albano, Rochester Institute of Technology (United States)
William F. Basener, Rochester Institute of Technology (United States)
William F. Basener, 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)
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