Name
Abstract | ||
|---|---|---|
Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/rs9040348 | REMOTE SENSING |
Keywords | Field | DocType |
statistical modeling,polarimetric SAR,texture models,finite mixture models,copulas | Data mining,Copula (linguistics),Remote sensing,Polarimetric synthetic aperture radar,Polarimetric sar,Statistical model,Geology,Probability density function,Statistical analysis | Journal |
Volume | Issue | Citations |
9 | 4 | 8 |
PageRank | References | Authors |
0.47 | 31 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinping Deng | 1 | 10 | 1.17 |
| Carlos Lopez-Martinez | 2 | 210 | 29.10 |
| Jinsong Chen | 3 | 49 | 11.29 |
| Pengpeng Han | 4 | 25 | 3.09 |