Name
Abstract | ||
|---|---|---|
Polarimetric imaging systems have shown promising applications in passive remote sensing. To better understand the effects of various system attributes and help optimize the de sign and use of polarimetric imaging systems, an analytical model is developed to predict the system performance. The model consists of scene, sensor, and processing system components. Some pre-processing procedures such as estimation of the Stokes vector and degree of linear polarization (DoLP) are currently considered in the processing model. Validation with data collected from a division of time polarimeter shows good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IGARSS.2011.6050108 | IGARSS |
Keywords | Field | DocType |
remote sensing,dolp,degree of linear polarization,system modeling,passive remote sensing,polarimetry,optical images,stokes vector,analytical modeling,optical polarimetric imaging systems,probability density functions,time polarimeter,optical polarization,probability,noise,process model,system performance,geometry,data collection,stokes parameters,imaging | Polarimetry,Stokes parameters,Computer science,Remote sensing,Polarization (waves),Optics,Linear polarization,Systems modeling,Optical polarization,Polarimeter | Conference |
ISSN | ISBN | Citations |
2153-6996 | 978-1-4577-1003-2 | 1 |
PageRank | References | Authors |
0.36 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingfei Meng | 1 | 2 | 1.41 |
| John P. Kerekes | 2 | 194 | 35.38 |