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

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 6 m × 4 m 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 >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.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2026-04-11
DOI
https://doi.org/10.1016/j.dib.2026.112773
Primary Topic
Oil Spill Detection and Mitigation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Guangmin Sun, Abdul Qadir Khan, Sohail Jamal
Data in Brief
Oil Spill Detection and Mitigation
article

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

Guangmin Sun, Abdul Qadir Khan, Sohail Jamal
article en

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 6 m × 4 m 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 >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.

Data in BriefVol. 66
Beijing University of Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Life below water
Openalex Percentile: Top 26%
Oil Spill Detection and Mitigation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.