Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification

We have developed a nonlocal algorithm for estimating polarimetric synthetic aperture radar (PolSAR) covariance matrices on single-look complex (SLC) format resolution. The algorithm is inspired by recent work with guided nonlocal means (NLM) speckle filtering, where a coregistered optical image is used to aid the filtering. Based on patchwise dissimilarities in the SAR and optical domains, we set the weights used for the nonlocal average of the outer product of the lexicographic target vectors that form the estimate. Using this method we show that the estimated covariance matrices preserve the local structure better than previous filtering methods and improve the separation of live from defoliated and dead forest. The details of the preserving nature of the algorithm also means that it can be applicable in other settings where preserving the SLC format resolution is necessary.