When crude oil spills into water, it spreads according to the movement of currents, wind, and waves, exacerbating the pollution. For example, the 2010 Deep Horizon oil spill released millions of barrels of oil from 1.5 km beneath the sea surface, eventually covering around 12,000 km2 of ocean.
Detecting and monitoring oil spills with satellite images has proven critical in addressing and minimizing oil spill hazards. However, merging data from various satellite images for more precise detection has been difficult until recently.
We combined inputs from two types of satellite: the synthetic aperture radar (SAR) and the hyperspectral imaging satellite (HSI). SAR can detect the differences in waves and surface roughness of the ocean. If there’s oil on the surface of the water, it’ll make the surface smoother. But alone, it often confuses thin versus thick oil. HSI acts like a super-detailed color sensor that helps determine what the spilled oil material is, but it doesn’t generalize as widely.
Dr. Kevin Huang, Senior Lecturer, James Cook University
Huang suggests that this new fused approach is superior to other methods, since it combines cleaner spill outlines and stronger oil-type recognition.
The researchers pragmatically propose a straightforward response strategy: employ SAR for quick, wide-area detection after a spill, then utilize the SAR+HSI fusion inside the detected region to assess the type and thickness of oil and inform cleanup decisions.
Both scientists contend that this strategy is crucial for dealing with oil spills and might also be useful for future monitoring in various environmental settings.
We want to further apply our deep learning and remote sensing research to other monitoring applications, like water quality, forests, and disasters, to benefit societies and communities.
Dr. Kevin Huang. Senior Lecturer, James Cook University
Journal Reference:
Liu, Q. et.al. (2025) Enhancing oil spill detection with controlled random sampling: A multimodal fusion approach using SAR and HSI imagery. Remote Sensing Applications: Society and Environment. doi.org/10.1016/j.rsase.2025.101601