Abstract
Despeckling of synthetic aperture radar (SAR) data is a challenge for high-resolution applications. This study presents a new pixel-based multitemporal nonlocal averaging (PIMSAR) approach to apply nonlocal mean filtering to ground range detected high (GRDH) resolution SAR images preserving the smallest details of the spatial resolution (10 m). The similarity of SAR pixels is based on the temporal evolution of nature using a two-step process. The mean and standard deviation of pixelwise intensity from spring to autumn are used as the basis of unsupervised classification of the area of interest. The nonlocal averaging is carried out within each class separately in magnitude order of the temporal averages. The filtered image shows the details that are indistinguishable in the original image. The kurtosis of the filtered image is close to that of a corresponding airborne image. PIMSAR preserves the mean intensity of the image with a relative accuracy better than 0.02%, and yet, the processing is rapid per image and the method is easy to use.
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