Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel ship detection approach in polarimetric synthetic aperture radar (SAR) images via variational Bayesian inference. First, we express the polarimetric SAR image as a tensor, and decompose the SAR image as the sum of a sparse component associated with ships and a sea clutter component. These components are denoted by some latent variables. Then, we introduce hierarch... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTARS.2017.2687473 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Marine vehicles,Bayes methods,Synthetic aperture radar,Clutter,Tensile stress,Scattering,Indexes | Computer vision,Bayesian inference,Clutter,Synthetic aperture radar,Remote sensing,Inverse synthetic aperture radar,Latent variable,Pixel,Artificial intelligence,Statistical model,Prior probability,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 6 | 1939-1404 |
Citations | PageRank | References |
1 | 0.35 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Song | 1 | 37 | 5.63 |
| Bin Xu | 2 | 133 | 23.23 |
| Jian Yang | 3 | 483 | 64.80 |