SAR Image Denoising using MMSE Techniques

Synthetic aperture radar (SAR) provides many advantages over optical remote sensing, principally the all-weather and all-day acquisition capability. For this reason, SAR images have been exploited for many applications such as forestry, agriculture, disaster monitoring, sea/ice monitoring. However, the main limitation in SAR images is the contamination with the multiplicative speckle noise. The speckle damages the radiometric quality of SAR images and contracts the performance of information extraction techniques. Many methods have been proposed in the literature to reduce speckle noise. These methods, however, must avoid degrading the useful information in the SAR images, such as textures, local mean of backscatter, and point targets. The minimum mean square error (MMSE) techniques have been largely applied in SAR image speckle denoising. The objective of this chapter is to review and give new insights into the MMSE denoising of SAR images. In particular, the performances of three MMSE-based techniques which are the commonly applied Lee sigma filter and the newly introduced iterative MMSE (IMMSE) filter, and the infinite number of looks prediction (INLP) filter are studied.