Adaptive stack filters in speckled imagery
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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| Autores principales: | , , , |
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| Formato: | CONF |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97288654_v1_n_p33_Buemi |
| Aporte de: |
| Sumario: | 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 using a boolean function. Adaptive stack filters are optimized filters that compute a boolean function by using a corrupted image and ideal image without noise. In this work the behaviour of an adaptive stack filter is evaluated for the classification of synthetic apreture radar (SAR) images, which are affected by speckle noise. With this aim it is carried out a Monte Carlo experiment in which simulated images are generated and then filtered with a stack filter trained with one of them. The results of their maximum likelihood classification are evaluated and then are compared with the results of classifying the images without previous filtering. |
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