Clustering of Marine Oil-Spill Extent Using Sentinel-1 Dual Polarimetric Scattering Spectrum

Citation

Paul A, Dey S, Marino A, Bhowmick GD & Bhattacharya A (2023) Clustering of Marine Oil-Spill Extent Using Sentinel-1 Dual Polarimetric Scattering Spectrum. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 8923-8932. https://doi.org/10.1109/jstars.2023.3314899

Abstract
Oil spills pose a significant threat to the maritime ecosystem. Identifying an oil spill is vital to assess its spread and drift to nearby coastal areas. Synthetic aperture radar (SAR) sensors are viable for mapping and monitoring marine oil spills. This study proposes a new technique that utilizes the dual-polarimetric Sentinel-1 SAR data. The method is based on projecting the 2 × 2 covariance matrix onto distinct random realizations of the normalized scattering configuration. We then obtain the dual-polarimetric spectrum of the scattering-type parameter, θDP. The θDP spectrum is then used in the unsupervised K-means clustering technique to segment oil spills from the rest. The cluster findings are then compared to the accuracies obtained using the standard scattering-type parameters from the eigen-decomposition approach (VV, VH) intensities and Otsu thresholding of [H + α + A] parameter. We demonstrate the proposed approach by clustering marine oil-spill extent over parts of India, Kuwait, the UAE, and the Mediterranean Sea obtained by Sentinel-1 SAR images. We observed that the clustering accuracy of the proposed technique outperforms the ones obtained from the channel (i.e., VV and VH) intensities, Otsu thresholding of [H + α + A] parameter, and the eigen-decomposition-based method. The proposed approach improves the overall accuracy by ≈8% and ≈20%, respectively, over different study areas.

Keywords
Dual-polarized synthetic aperture radar (SAR); K-means clustering; oil spill (OS); Sentinel-1; unsupervised classification

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing: Volume 16