Speckle Reduction using Stochastic Distances

TL;DR

This paper introduces a novel speckle reduction filter for SAR images based on stochastic distances and goodness-of-fit tests, demonstrating improved noise reduction and edge preservation compared to standard methods.

Contribution

The paper proposes a new filter design using stochastic distances and statistical tests, applied to SAR data with adaptive windowing, enhancing speckle reduction while maintaining image details.

Findings

Outperforms Lee's filter in noise reduction and edge preservation.

Effectively adapts to different heterogeneity levels in SAR data.

Improves image quality metrics such as SNR and correlation.

Abstract

This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Aperture Radar (SAR) data, using the Gamma model with varying number of looks allowing, thus, changes in heterogeneity. Modified Nagao-Matsuyama windows are used to define the samples. The proposal is compared with the Lee's filter which is considered a standard, using a protocol based on simulation. Among the criteria used to quantify the quality of filters, we employ the equivalent number of looks (related to the signal-to-noise ratio), line contrast, and edge preservation. Moreover, we also assessed the filters by the Universal Image Quality Index and the Pearson's correlation between edges.