Vehicle track segmentation using higher order random fields

Journal Article · Mon Jan 09 00:00:00 EST 2017 · IEEE Geoscience and Remote Sensing Letters

DOI:https://doi.org/10.1109/LGRS.2016.2643564· OSTI ID:1338399

Quach, Tu -Thach  ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1338399
• Report Number(s): SAND-2016-12839J; 650058
• Journal Information: IEEE Geoscience and Remote Sensing Letters, Vol. PP, Issue 99; ISSN 1545-598X
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
vehicle track
image segmentation
random fields
synthetic aperture imaging