Name
Abstract | ||
|---|---|---|
This paper presents a general approach for high- resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test. The new approach provide a threshold over which pixels are rejected from the image, meaning they are not sufficiently “close” from any existing class. A distance measure using this general approach is derived and tested on a high-resolution polarimetric data set acquired by the ONERA RAMSES system. It is compared to the results of the classical H-α decomposition and Wishart classifier under Gaussian and SIRV assumption. Results show that the new approach rejects all pixels from heterogeneous parts of the scene and classifies its Gaussian parts. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/JSTSP.2010.2101579 | Selected Topics in Signal Processing, IEEE Journal of |
Keywords | DocType | Volume |
covariance matrices,radar clutter,radar imaging,radar resolution,statistical analysis,synthetic aperture radar,Gaussian assumption,H-α decomposition,ONERA RAMSES system,Wishart classifier,covariance matrices,heterogeneous clutter,high-resolution polarimetric SAR data classification,spherically invariant random vector model,standard classification methods,statistical classification,synthetic aperture radar,Image classification,non-Gaussian modeling,polarimetric synthetic aperture radar,statistical analysis | Journal | 5 |
Issue | ISSN | Citations |
3 | 1932-4553 | 22 |
PageRank | References | Authors |
1.12 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Formont, P. | 1 | 22 | 1.12 |
| Frédéric Pascal | 2 | 175 | 23.99 |
| Vasile, G. | 3 | 44 | 5.62 |
| Jean Philippe Ovarlez | 4 | 190 | 25.11 |