Name
Abstract | ||
|---|---|---|
In this letter, we propose a novel ship detection method in synthetic aperture radar (SAR) imagery via variational Bayesian inference. First, we establish the ship detection probabilistic model which decomposes the SAR image as the sum of a sparse component associated with ships and a sea clutter component. Then, we introduce hierarchical priors of the latent variables in the model and use variational Bayesian inference to estimate the posterior distributions of the latent variables. The proposed method is an automatic iterative process without any sliding window. Experimental results accomplished over synthetic data and a RADARSAT-2 SAR image demonstrate that the proposed method can achieve state-of-the-art ship detection performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2015.2510378 | Geoscience and Remote Sensing Letters, IEEE |
Keywords | Field | DocType |
Ship detection,synthetic aperture radar (SAR),variational Bayesian inference | Bayesian inference,Synthetic aperture radar,Remote sensing,Synthetic data,Artificial intelligence,Computer vision,Sliding window protocol,Pattern recognition,Clutter,Inverse synthetic aperture radar,Statistical model,Prior probability,Mathematics | Journal |
Volume | Issue | ISSN |
PP | 99 | 1545-598X |
Citations | PageRank | References |
6 | 0.48 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Song | 1 | 37 | 5.63 |
| Bin Xu | 2 | 133 | 23.23 |
| zenghui li | 3 | 29 | 2.35 |
| Jian Yang | 4 | 483 | 64.80 |