Assessment of SAR Image Filtering using Adaptive Stack Filters
This work addresses noise reduction in SAR images for remote sensing applications, but it appears incremental as it applies an existing adaptive method to a specific domain.
The study evaluated adaptive stack filters for filtering Synthetic Aperture Radar (SAR) images by assessing filtered image quality and classification accuracy.
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 then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images.