A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stack filters, stack smoothers, and mirrored thresholddecomposition
IEEE Transactions on Signal Processing
Fast algorithms for training stack filters
IEEE Transactions on Signal Processing
Stack filter design using selection probabilities
IEEE Transactions on Signal Processing
A fast algorithm for designing stack filters
IEEE Transactions on Image Processing
An Exact Algorithm for Optimal MAE Stack Filter Design
IEEE Transactions on Image Processing
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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.