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...
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v31_n4_p307_Buemi http://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi |
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paper:paper_01678655_v31_n4_p307_Buemi2023-06-08T15:16:59Z SAR image processing using adaptive stack filter Buemi, María Elena Mejail, Marta Estela 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. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v31_n4_p307_Buemi 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, María Elena Mejail, Marta Estela 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. |
author |
Buemi, María Elena Mejail, Marta Estela |
author_facet |
Buemi, María Elena Mejail, Marta Estela |
author_sort |
Buemi, María Elena |
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 |
publishDate |
2010 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v31_n4_p307_Buemi http://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi |
work_keys_str_mv |
AT buemimariaelena sarimageprocessingusingadaptivestackfilter AT mejailmartaestela sarimageprocessingusingadaptivestackfilter |
_version_ |
1768545252722868224 |