Improved Polarimetric LEE Filter Using the similarity of Structure

Reducing the speckle noise and keeping the detail are conflicted because of the parameters in the polarimetric Lee filter. This paper analyzes nonlocal means filter method, presents an optimization polarimetric Lee filter based on Bayesian nonlocal means. This algorithm calculates the similarity coefficient of the elements in SPAN first, then the weights are carried out to get expectation and the parameter in Lee filter, averages the covariance matrices with the weights according to the similarity between the elements, finally all terms of the covariance matrix will be filtered by Lee filter. Compared with traditional algorithm, the experimental results based on single-look and multi-look data show that the algorithm predigests the complexity of nonlocal means, keeps the polarimetric information well, and it also has the advantages of effectively reducing the speckle noise and keeping the detail.