Name
Title | ||
|---|---|---|
Synthetic aperture radar image segmentation using non-linear diffusion-based hierarchical triplet Markov fields model. |
Abstract | ||
|---|---|---|
Triplet Markov fields (TMF) model is widely used to deal with non-stationary synthetic aperture radar (SAR) images. However, its ability to capture global information remains limited due to the non-causal property. A hierarchical TMF model is proposed in this study based on the non-linear diffusion (ND) strategy, which is denoted as ND-hierarchical TMF (HTMF). ND is adopted to generate multiscale ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1049/iet-ipr.2016.0901 | IET Image Processing |
Keywords | Field | DocType |
image denoising,image segmentation,Markov processes,radar imaging,speckle,synthetic aperture radar | Computer vision,Force field (chemistry),Scale-space segmentation,Pattern recognition,Feature (computer vision),Synthetic aperture radar,Segmentation,Markov chain,Robustness (computer science),Artificial intelligence,Speckle noise,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 12 | 1751-9659 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Wang | 1 | 3 | 1.38 |
| Yan Wu | 2 | 226 | 27.81 |
| Peng Zhang | 3 | 11 | 2.57 |
| Wenkai Liang | 4 | 3 | 1.73 |
| Ming Li | 5 | 2 | 2.38 |