Name
Title | ||
|---|---|---|
Unsupervised Speckle Level Estimation Of Sar Images Using Texture Analysis And Ar Model |
Abstract | ||
|---|---|---|
In this paper, a new method is proposed for unsupervised speckle level estimation in synthetic aperture radar (SAR) images. It is assumed that fully developed speckle intensity has a Gamma distribution. Based on this assumption, estimation of the equivalent number of looks (ENL) is transformed into noise variance estimation in the logarithmic SAR image domain. In order to improve estimation accuracy, texture analysis is also applied to exclude areas where speckle is not fully developed (e.g., urban areas). Finally, the noise variance is estimated by a 2-dimensional autoregressive (AR) model. The effectiveness of the proposed method is verified with several SAR images from different SAR systems and simulated images. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transcom.E97.B.691 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | Field | DocType |
AR model, equivalent number of looks (ENL), synthetic aperture radar (SAR), texture analysis | Computer vision,Autoregressive model,Speckle pattern,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E97B | 3 | 1745-1345 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Xu | 1 | 133 | 23.23 |
| Yi Cui | 2 | 2 | 0.37 |
| Guangyi Zhou | 3 | 42 | 3.93 |
| Biao You | 4 | 6 | 0.89 |
| Jian Yang | 5 | 483 | 64.80 |
| Jianshe Song | 6 | 24 | 4.70 |