Synthetic aperture radar (SAR) is traditionally used in the identification, mapping and analysis of petroleum slicks, regardless of their origin. On SAR images, oil slicks appear as dark patches that contrast with the brightness of the surrounding sea surface. This distinction allows for automated detection algorithms to be designed using computer vision methods for objective oil slick identification. Nevertheless, efficient interpretation of the SAR imagery by statistical analysis can be diminished due to the speckle effect present on SAR images, a granular artefact associated with the coherent nature of SAR that visually degrades the image quality. In this study, a quantitative and qualitative assessment of common SAR image despeckling methods is presented, analysing their performance when applied to images containing natural oil slicks. The assessment is performed on Copernicus Sentinel-1 images acquired with various temporal and environmental conditions. The assessment covers a diverse array of filters that employ Bayesian and non-linear statistics in the spatial, transform and wavelet domains, focussing on their demonstrated performance and capabilities for edge and texture retention. In summary, the results reveal that filters using local statistics in the spatial domain produce consistent desired effects. The novel SAR-BM3D algorithm can be used effectively, albeit with a higher computational demand.

Supplementary material: Implementations of the speckle filters used in this paper are made available at under an MIT license. Image statistics data are available in the supplementary table at

Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at:

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