Name
Title | ||
|---|---|---|
Inshore Ship Detection via Saliency and Context Information in High-Resolution SAR Images. |
Abstract | ||
|---|---|---|
So far, the detection of ships on sea has been widely studied, while fewer works are available on inshore ship detection. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective detection of inshore ships. In this letter, we present a novel approach via saliency and context information to deal with this i... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2616187 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Marine vehicles,Synthetic aperture radar,Feature extraction,Context,Image segmentation,Clutter,Optical imaging | Computer vision,False alarm,Salience (neuroscience),Synthetic aperture radar,Clutter,Remote sensing,Feature extraction,Image segmentation,Robustness (computer science),Artificial intelligence,Mathematics,Salient | Journal |
Volume | Issue | ISSN |
13 | 12 | 1545-598X |
Citations | PageRank | References |
3 | 0.42 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Zhai | 1 | 3 | 0.42 |
| Yu Li | 2 | 14 | 0.95 |
| Yi Su | 3 | 17 | 2.04 |