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Title: Vehicle track segmentation using higher order random fields

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure on a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.
Authors:
ORCiD logo [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
OSTI Identifier:
1338399
Report Number(s):
SAND-2016-12839J
Journal ID: ISSN 1545-598X; 650058
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
IEEE Geoscience and Remote Sensing Letters
Additional Journal Information:
Journal Volume: PP; Journal Issue: 99; Journal ID: ISSN 1545-598X
Publisher:
IEEE
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; vehicle track; image segmentation; random fields; synthetic aperture imaging