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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Autor principal: Buemi, M.E
Otros Autores: Jacobo, J., Mejail, M.
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2010
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Acceso en línea:Registro en Scopus
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024 7 |2 scopus  |a 2-s2.0-74649086416 
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100 1 |a Buemi, M.E. 
245 1 0 |a SAR image processing using adaptive stack filter 
260 |c 2010 
270 1 0 |m Buemi, M.E.; Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Ciudad Universitaria, Pabellon I, C1428EGA Buenos Aires, Argentina; email: mebuemi@dc.uba.ar 
506 |2 openaire  |e Política editorial 
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504 |a Dellamonica Jr., D., Silva, P.J.S., Humes Jr., C., Hirata, N.S.T., Barrera, J., An exact algorithm for optimal MAE stack filter design (2007) IEEE Trans. Image Process., 16 (2), pp. 453-462 
504 |a Diaz, D., Paredes, J.L., FPGA implementation of a new family of stack filters (2004) Proc. 5th IEEE Internat. Caracas Conf. on Devices, Circuits and Systems, 1, pp. 152-157 
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504 |a Frery, A.C., Correia, A.H., Rennó, C.D., Freitas, C.C., Jacobo-Berlles, J., Mejail, M.E., Vasconcellos, K.L.P., Sant'anna, S.J., Models for synthetic aperture radar image analysis (1999) Resenhas (IME-USP), 4 (1), pp. 45-77 
504 |a Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C., A model for radar images and its application to adaptive digital filtering of multiplicative noise (1982) IEEE Trans. Pattern Anal. Machine Intell., 4, pp. 157-166 
504 |a Goodman, J.W., Some fundamental properties of speckle (1976) J. Opt. Soc. Am., 66, pp. 1145-1150 
504 |a Lee, J.-S., Refined filtering of image noise using local statistics (1981) Comput. Graph. Image Process., 15 (4), pp. 380-389 
504 |a Lin, H.-J., Sellke, T.M., Coyle, E.J., Adaptive stack filtering under the mean absolute error criterion (1990) IEEE Trans. Acoust. Speech Signal Process., 38, pp. 938-954 
504 |a Lin, H.-J., Kim, Y.T., Fast algorithms for training stack filters (1994) IEEE Trans. Signal Process., 42 (3), pp. 772-781 
504 |a Lopés, A., Nezry, E., Touzi, R., Laur, H., Structure detection and statistical adaptive speckle filtering in SAR images (1993) Internat. J. Remote Sens., 14 (9), pp. 1735-1758 
504 |a Mejail, M.E., Jacobo-Berlles, J., Frery, A.C., Bustos, O.H., Classification of SAR images using a general and tractable multiplicative model (2003) Internat. J. Remote Sens., 24 (18), pp. 3565-3582 
504 |a Oliver, C., Quegan, S., (1998) Understanding Synthetic Aperture Radar Images, , Artech House 
504 |a Paredes, J., Arce, G., Stack filters, stack smoothers, and mirrored threshold decomposition (1999) IEEE Trans. Signal Process., 47 (10), pp. 2757-2767 
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520 3 |a 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.  |l eng 
593 |a Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación, Ciudad Universitaria, Pabellon I, C1428EGA Buenos Aires, Argentina 
690 1 0 |a CLASSIFICATION 
690 1 0 |a SPECKLE 
690 1 0 |a STACK FILTERS 
690 1 0 |a SYNTHETIC APERTURE RADAR 
690 1 0 |a EQUIVALENT NUMBER OF LOOKS 
690 1 0 |a INPUT IMAGE 
690 1 0 |a MAXIMUM LIKELIHOOD CLASSIFICATIONS 
690 1 0 |a NOISELESS IMAGES 
690 1 0 |a NOISY VERSIONS 
690 1 0 |a NONLINEAR FILTER 
690 1 0 |a SAR IMAGE PROCESSING 
690 1 0 |a SAR IMAGES 
690 1 0 |a SPECKLE NOISE REDUCTION 
690 1 0 |a STACK FILTERS 
690 1 0 |a BINARY IMAGES 
690 1 0 |a BOOLEAN FUNCTIONS 
690 1 0 |a IMAGE CLASSIFICATION 
690 1 0 |a IMAGING SYSTEMS 
690 1 0 |a MAXIMUM LIKELIHOOD 
690 1 0 |a SPECKLE 
690 1 0 |a SYNTHETIC APERTURES 
690 1 0 |a SYNTHETIC APERTURE RADAR 
650 1 7 |2 spines  |a RADAR 
700 1 |a Jacobo, J. 
700 1 |a Mejail, M. 
773 0 |d 2010  |g v. 31  |h pp. 307-314  |k n. 4  |p Pattern Recogn. Lett.  |x 01678655  |t Pattern Recognition Letters 
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856 4 0 |u https://doi.org/10.1016/j.patrec.2009.02.008  |y DOI 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_01678655_v31_n4_p307_Buemi  |y Handle 
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