SAR image processing using adaptive stack filter
Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filte...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi |
| Aporte de: |
| id |
todo:paper_01678655_v31_n4_p307_Buemi |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_01678655_v31_n4_p307_Buemi2023-10-03T15:05:25Z SAR image processing using adaptive stack filter Buemi, M.E. Jacobo, J. Mejail, M. Classification Speckle Stack filters Synthetic aperture radar Equivalent number of looks Input image Maximum likelihood classifications Noiseless images Noisy versions Nonlinear filter SAR image processing SAR Images Speckle noise reduction Stack filters Binary images Boolean functions Image classification Imaging systems Maximum likelihood Radar Speckle Synthetic apertures Synthetic aperture radar Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters. © 2009 Elsevier B.V. All rights reserved. Fil:Buemi, M.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mejail, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Classification Speckle Stack filters Synthetic aperture radar Equivalent number of looks Input image Maximum likelihood classifications Noiseless images Noisy versions Nonlinear filter SAR image processing SAR Images Speckle noise reduction Stack filters Binary images Boolean functions Image classification Imaging systems Maximum likelihood Radar Speckle Synthetic apertures Synthetic aperture radar |
| spellingShingle |
Classification Speckle Stack filters Synthetic aperture radar Equivalent number of looks Input image Maximum likelihood classifications Noiseless images Noisy versions Nonlinear filter SAR image processing SAR Images Speckle noise reduction Stack filters Binary images Boolean functions Image classification Imaging systems Maximum likelihood Radar Speckle Synthetic apertures Synthetic aperture radar Buemi, M.E. Jacobo, J. Mejail, M. SAR image processing using adaptive stack filter |
| topic_facet |
Classification Speckle Stack filters Synthetic aperture radar Equivalent number of looks Input image Maximum likelihood classifications Noiseless images Noisy versions Nonlinear filter SAR image processing SAR Images Speckle noise reduction Stack filters Binary images Boolean functions Image classification Imaging systems Maximum likelihood Radar Speckle Synthetic apertures Synthetic aperture radar |
| description |
Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters. © 2009 Elsevier B.V. All rights reserved. |
| format |
JOUR |
| author |
Buemi, M.E. Jacobo, J. Mejail, M. |
| author_facet |
Buemi, M.E. Jacobo, J. Mejail, M. |
| author_sort |
Buemi, M.E. |
| title |
SAR image processing using adaptive stack filter |
| title_short |
SAR image processing using adaptive stack filter |
| title_full |
SAR image processing using adaptive stack filter |
| title_fullStr |
SAR image processing using adaptive stack filter |
| title_full_unstemmed |
SAR image processing using adaptive stack filter |
| title_sort |
sar image processing using adaptive stack filter |
| url |
http://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi |
| work_keys_str_mv |
AT buemime sarimageprocessingusingadaptivestackfilter AT jacoboj sarimageprocessingusingadaptivestackfilter AT mejailm sarimageprocessingusingadaptivestackfilter |
| _version_ |
1782027764776304640 |