Name
Abstract | ||
|---|---|---|
Combining multiple synthetic aperture radar (SAR) images taken at different times of the same scene produces coherent change detection (CCD) images that can detect small surface changes such as tire tracks. The resulting CCD images can be used in an automated approach to identify and label tracks. Existing techniques have limited success due to the noisy nature of these CCD images. In particular, existing techniques require some user cues and can only trace a single track. This paper presents an approach to automatically identify and label multiple tracks in CCD images. We use an explicit objective function that utilizes the Bayesian information criterion to find the simplest set of curves that explains the observed data. Experimental results show that it is capable of identifying tracks under various scenes and can correctly declare when no tracks are present. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CVPRW.2015.7301295 | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
model-based approach,SAR CCD image,synthetic aperture radar image,coherent change detection image,CCD image,explicit objective function,Bayesian information criterion | Pulse-Doppler radar,Computer vision,Radar imaging,Bayesian information criterion,Radar tracker,Pattern recognition,Computer science,Synthetic aperture radar,Side looking airborne radar,Inverse synthetic aperture radar,Artificial intelligence,Fire-control radar | Conference |
Volume | Issue | ISSN |
2015 | 1 | 2160-7508 |
Citations | PageRank | References |
2 | 0.43 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tu-Thach Quach | 1 | 35 | 6.68 |
| Rebecca Malinas | 2 | 3 | 1.13 |
| Mark W. Koch | 3 | 92 | 10.60 |