Feature-Based Nonlocal Polarimetric SAR Filtering

Abstract

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance (CV) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

“…This equation transforms the DN of each pixel into a backscattering coefficient on a linear scale. A refined Lee filter [43,44] was then applied with a window of 7 × 7 pixels to reduce speckle noise using SNAP v5.0 software. This window size was selected to decrease speckle noise while preserving a suitable spatial scale, which was necessary to ensure identification of winter land use.…”

“…Polarimetric Parameters A 2 × 2 covariance matrix ( ) was first extracted from the scattering matrix S of each SLC image using PolSARpro v5.1.1 software [45]. The elements of the matrix, which are independent of the A refined Lee filter [43,44] was then applied with a window of 7 × 7 pixels to reduce speckle noise using SNAP v5.0 software. This window size was selected to decrease speckle noise while preserving a suitable spatial scale, which was necessary to ensure identification of winter land use.…”

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

“…Also, the extension of this method, named CCM+SimiTest, which proposed the new context covariance matrix formulation and a fast similarity test computation scheme, achieved more satisfying performance on speckle reduction and details preservation than local and nonlocal filters [49]. Yet another approach, is to use the coefficient of variance and Pauli basis (CVPB-NLM) to measure the similarity, which showed higher ENL metrics than the refined Lee filter [50].…”

Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification

“…( 8), or in a similar weighted average. While speckle should be suppressed, the polarimetric information should also be preserved [25]. The general idea is to avoid mixing pixels belonging to different structures, so that edges and strong scatterers are preserved by not averaging such pixels with their heterogeneous background or adjacent areas [26].…”

“…Another example of nonlocal filtering of covariance matrices is [25], where a combination of two similarity measures between coherence matrices was used to calculate the weights. One was based on the Pauli decomposition and the other on the coefficient of variation.…”