Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification

TL;DR

This paper introduces a guided nonlocal means algorithm for estimating polarimetric SAR covariance matrices, enhancing local structure preservation and improving forest state classification accuracy.

Contribution

The paper presents a novel guided nonlocal means method that uses optical images to improve covariance matrix estimation in PolSAR data, maintaining resolution and structure.

Findings

Better preservation of local structure compared to previous methods

Improved separation of live, defoliated, and dead forests

Applicable to other settings requiring SLC resolution preservation

Abstract

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 co-registered optical image is used to aid the filtering. Based on patch-wise 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 which form the estimate. By use of 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 detail preserving nature of the algorithm also means that it can be applicable in other settings where preserving the SLC format resolution is necessary.