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Title: Anomalous change detection in imagery

Abstract

A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.

Inventors:
 [1];  [2]
  1. Los Alamos, NM
  2. Santa Fe, NM
Issue Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1019392
Patent Number(s):
7953280
Application Number:
11/871,714
Assignee:
Los Alamos National Security LLC (Los Alamos, NM)
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Theiler, James P, and Perkins, Simon J. Anomalous change detection in imagery. United States: N. p., 2011. Web.
Theiler, James P, & Perkins, Simon J. Anomalous change detection in imagery. United States.
Theiler, James P, and Perkins, Simon J. Tue . "Anomalous change detection in imagery". United States. https://www.osti.gov/servlets/purl/1019392.
@article{osti_1019392,
title = {Anomalous change detection in imagery},
author = {Theiler, James P and Perkins, Simon J},
abstractNote = {A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 31 00:00:00 EDT 2011},
month = {Tue May 31 00:00:00 EDT 2011}
}

Works referenced in this record:

Resampling approach for anomalous change detection
conference, April 2007


Change Detection in Overhead Imagery Using Neural Networks
journal, March 2003