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Title: Data Analytics for SAR

Abstract

We assess the ability of variants of anomalous change detection (ACD) to identify human activity associated with large outdoor music festivals as they are seen from synthetic aperture radar (SAR) imagery collected by the Sentinel-1 satellite constellation. We found that, with appropriate feature vectors, ACD using random-forest machine learning was most effective at identifying changes associated with the human activity.

Authors:
 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1396159
Report Number(s):
LA-UR-17-28988
DOE Contract Number:
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Murphy, David Patrick, and Calef, Matthew Thomas. Data Analytics for SAR. United States: N. p., 2017. Web. doi:10.2172/1396159.
Murphy, David Patrick, & Calef, Matthew Thomas. Data Analytics for SAR. United States. doi:10.2172/1396159.
Murphy, David Patrick, and Calef, Matthew Thomas. Mon . "Data Analytics for SAR". United States. doi:10.2172/1396159. https://www.osti.gov/servlets/purl/1396159.
@article{osti_1396159,
title = {Data Analytics for SAR},
author = {Murphy, David Patrick and Calef, Matthew Thomas},
abstractNote = {We assess the ability of variants of anomalous change detection (ACD) to identify human activity associated with large outdoor music festivals as they are seen from synthetic aperture radar (SAR) imagery collected by the Sentinel-1 satellite constellation. We found that, with appropriate feature vectors, ACD using random-forest machine learning was most effective at identifying changes associated with the human activity.},
doi = {10.2172/1396159},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Oct 02 00:00:00 EDT 2017},
month = {Mon Oct 02 00:00:00 EDT 2017}
}

Technical Report:

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