The presence of speckle noise degrades the quality of polarimetric synthetic aperture radar (PolSAR) images. Suppressing speckle is an essential preprocessing step when using SAR data. In this study

a technique based on a nonlocal weighted linear minimum mean squared error (LMMSE) filter is proposed for filtering speckle in polarimetric SAR images. The concept of nonlocal means is employed in the proposed method to evaluate the weights of the samples in the LMMSE estimator. In the pixel sample selection process

the polarimetric scattering property and heterogeneity of the neighboring patch of the processed pixel are utilized to discard unrelated pixels. The algorithm is accelerated while preserving the point targets and adaptively changing the size of the patch window. Experiments on polarimetric SAR images show that the quality of the images filtered by the proposed algorithm is improved. Compared with the number of looks for images filtered by traditional LMMSE filters

that for images filtered by the proposed method increases by over 10 looks for single-look or multi-look speckled images. The peak signal-to-noise ratio is also increased by 5.8 dB. Overall classification accuracy of the filtered images is higher than 83%. The proposed method is more computationally efficient than nonlocal means algorithms. The proposed filter effectively reduces speckle and preserves edges

features

and the polarimetric scattering mechanism

and thus

supports the efficient use of SAR data.