Accession Number:

ADA586631

Title:

Online Cluster Analysis Supporting Real Time Anomaly Detection in Hyperspectral Imagery

Descriptive Note:

Graduate research paper

Corporate Author:

AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT

Personal Author(s):

Report Date:

2013-06-01

Pagination or Media Count:

172.0

Abstract:

Ongoing work in anomaly detection in hyperspectral images has shown that cluster analysis performed in appropriate principal component subspaces can enhance the performance of detectors such as the Reed-Xiaoli detector and its derivatives. Numerous operational considerations motivate the development of an online or incremental clustering algorithm, which can perform clustering as pixels of the image are collected in real time rather than waiting until the full image is complete. Such an algorithm is developed by combining key elements of existing clustering algorithms from related domains. The parameters of the algorithm are tuned and performance of the algorithm is assessed using a set of actual hyperspectral images by exploiting key attributes of an appropriate principal component sub-space. A byproduct of the clustering algorithm is a rudimentary anomaly detector which demonstrates the feasibility of cluster based outlier detection in hyperspectral imagery.

Subject Categories:

  • Theoretical Mathematics
  • Operations Research
  • Computer Hardware
  • Atomic and Molecular Physics and Spectroscopy

Distribution Statement:

APPROVED FOR PUBLIC RELEASE