Speckle Reduction using Stochastic Distances
This work addresses speckle noise reduction in SAR imagery, which is important for remote sensing applications, but it appears incremental as it builds on existing filtering concepts with a novel statistical approach.
The paper tackles speckle reduction in Synthetic Aperture Radar (SAR) images by introducing a filter design based on stochastic distances and goodness-of-fit tests, achieving improved performance over the standard Lee's filter as measured by criteria like equivalent number of looks and edge preservation.
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Aperture Radar (SAR) data, using the Gamma model with varying number of looks allowing, thus, changes in heterogeneity. Modified Nagao-Matsuyama windows are used to define the samples. The proposal is compared with the Lee's filter which is considered a standard, using a protocol based on simulation. Among the criteria used to quantify the quality of filters, we employ the equivalent number of looks (related to the signal-to-noise ratio), line contrast, and edge preservation. Moreover, we also assessed the filters by the Universal Image Quality Index and the Pearson's correlation between edges.