Modeling non-Rayleigh speckle distribution in SAR images
In non-Rayleigh distributed radar images, the number of scatterers can be viewed as a Poisson distributed random variable, with the mean itself random. When this mean is Gamma distributed, then the image classically satisfies the K distribution. We add three new possible distributions for this mean:...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2002-06, Vol.40 (6), p.1430-1435 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Delignon, Y. Pieczynski, W. |
| description | In non-Rayleigh distributed radar images, the number of scatterers can be viewed as a Poisson distributed random variable, with the mean itself random. When this mean is Gamma distributed, then the image classically satisfies the K distribution. We add three new possible distributions for this mean: inverse Gamma, Beta of the first kind, and Beta of the second kind. We show that new intensity distributions so obtained can be estimated, with the interest of the extension validated on a real image. |
| doi_str_mv | 10.1109/TGRS.2002.800234 |
| format | Article |
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| identifier | ISSN: 0196-2892 |
| ispartof | IEEE transactions on geoscience and remote sensing, 2002-06, Vol.40 (6), p.1430-1435 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied geophysics Beta Earth sciences Earth, ocean, space Exact sciences and technology Exponential distribution Internal geophysics Inverse Radar Radar imaging Radar scattering Random variables Rayleigh scattering Remote sensing Rough surfaces Speckle Surface roughness Surface waves Synthetic aperture radar |
| title | Modeling non-Rayleigh speckle distribution in SAR images |
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