The performance of speckle filters on Copernicus Sentinel-1 SAR images containing natural oil slicks

Author:

Vrînceanu Cristina Andra1ORCID,Grebby Stephen1ORCID,Marsh Stuart1

Affiliation:

1. Nottingham Geospatial Institute, University of Nottingham, 30 Triumph Road, Lenton, Nottingham NG7 2TU, UK

Abstract

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 https://github.com/cavrinceanu/specklefilters under an MIT license. Image statistics data are available in the supplementary table at https://doi.org/10.6084/m9.figshare.13010405 Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/cc/remote-sensing-for-site-investigations-on-earth-and-other-planets

Funder

University of Nottingham

Publisher

Geological Society of London

Subject

Earth and Planetary Sciences (miscellaneous),Geology,Geotechnical Engineering and Engineering Geology

Reference83 articles.

1. SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling

2. Alpers, W. and Espedal, H. 2004. Oils and surfactants. In: Jackson, C.R. and Apel, J.R. (eds) Synthetic Aperture Radar: Marine User's Manual. National Oceanic and Atmospheric Administration, US Department of Commerce, Washington, DC, 263–276.

3. Apel, J.R. 2004. Oceanic internal waves and solitons. In: Jackson, C.R. and Apel, J.R. (eds) Synthetic Aperture Radar: Marine User's Manual. National Oceanic and Atmospheric Administration, US Department of Commerce, Washington, DC, 189–207.

4. A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

5. Bharaneswari M. Arulmozhivarman P. Tatavarti R. and Senthilnathan K. 2015. Speckle noise suppression in SAR images (Oil spill images) using wavelet based methods and ICA technique. In : Rangan S. and Pathari V. (eds) 2015 IEEE International Conference on Signal Processing Informatics Communication and Energy Systems (SPICES). IEEE Piscataway NJ https://doi.org/10.1109/SPICES.2015.7091521

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