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April 13, 2026Data in Brief0 citationsOpen Access

Quad‑polarization synthetic aperture radar dataset for marine oil spill and look‑alike segmentation

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SJSohail JamalBeijing University of TechnologyGSGuangmin SunYLYong LiBGI Group (China)

Key Points

  • This research aims to provide a comprehensive quad-polarization SAR dataset for effective oil spill detection and segmentation.
  • Collection of 41 multi-look complex SAR scenes acquired from 2010 to 2022.
  • Calculation of a 3 × 3 polarimetric coherence matrix for each scene using quad polarization channels.
  • Annotation of four classes: sea surface, oil spill, look-alike phenomena, and land based on over 80 million labeled pixels.
  • Use of a semi-automated workflow combining manual labeling and Random Forest-based label propagation.
  • Dataset comprises fully polarimetric L-band SAR scenes with a resolution of roughly 6m×4m.
  • Segmentation masks provide detailed pixel-level annotations for effective classification.
  • The dataset supports reproducible research and benchmarking for SAR-based analyses.

Abstract

The quad‑polarization oil spill detection dataset is a collection of fully polarimetric L‑band synthetic aperture radar scenes acquired by the NASA/JPL uninhabited aerial vehicle synthetic aperture radar (UAVSAR) over the U.S. Gulf of Mexico from 2010 to 2022. The dataset comprises 41 multi‑look complex SAR scenes with a spatial resolution of roughly 6m×4m and incidence angles between 27° and 67°. For each scene, a 3 × 3 polarimetric coherence matrix is computed using the quad polarization channels, (HH, HV, VH, VV) namely horizontal transmit horizontal receive, horizontal transmit vertical receive, vertical transmit horizontal receive, and vertical transmit vertical receive. This matrix is then represented as a nine channel real valued feature set. Pixel‑level segmentation masks annotate four classes: sea surface, oil spill, look‑alike phenomena and land with more than 80 million labelled pixels. Ground‑truth labels were generated with a semi‑automated workflow combining manual labelling, Random Forest‑based label propagation and manual refinement. QPOSD is intended to enable reproducible research, benchmarking and polarimetric feature analysis for SAR‑based oil spill and look‑alike segmentation. The dataset is accessible at DOI: https://doi.org/10.5281/zenodo.19258036 .

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Cite This Study

Jamal et al. (2026) studied this question.

synapsesocial.com/papers/69dc89473afacbeac03eb1cdhttps://doi.org/10.1016/j.dib.2026.112773
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Oil Spill Detection Performance in a Multitype Polarimetric-Feature Space Using a Polarimetric Synthetic Aperture Radar: A Comparative Analysis2026
  2. 2Dataset of oil slicks, look-alikes and remarkable SAR signatures obtained from Sentinel-1 data in the Eastern Mediterranean Sea2025
  3. 3Application of multi-resolution techniques, intelligent classification and semantic data fusion for the identification of oil spills in SAR imagery2026
  4. 4The Information Consistency Between Full- and Improved Dual-Polarimetric Mode SAR for Multiscenario Oil Spill Detection2025
  5. 5A Quad-Polarimetric SAR image reconstruction network and wetland classification fusing optical texture and polarization information2026