Spectral Unmixing Evaluation for Oil Spill Characterization

Abstract

Hyperspectral remote sensing exploits the optical properties of materials and provides detailed information about them. From a theoretical point of view, in case of oil spills, it cannot only detect and delineate them, but also provide information about the oil type and oil thickness, significantly contributing at the remediation stage of clean-up. In practice, many factors, either associated with the inherent characteristics of oil spills (oil type, quantity, weathering stage, etc.), or with environmental factors (sea bottom cover and depth, waves, etc.) affect the spectral signature of the oil, set constraints on the effectiveness of hyperspectral methods. In this study, the key factors that enable an airborne hyperspectral campaign to implement effective surveys for oil spill detection and characterization are investigated. Additionally, the study focuses on the assessment of environmental and slick parameters for which spectral unmixing-based methods successfully address the problem of oil spill detection and oil type and thickness estimation. For this purpose, study of the spectral behavior of the oil through laboratory measurements and measurements in the complex marine environment was a prerequisite and has initially been carried out. The results showed that almost all the measured spectral signatures as well as their variations can be extracted as endmembers from synthetic images using the unmixing theory. Consequently, laboratory spectral libraries could enable the labeling procedure during the spectral unmixing application on hyperspectral imagery. Unfortunately, oil spectral measurements implemented in marine environment were significantly different because they were affected either by sea bottom contributions (case of light oil and petroleum products) or by sea state conditions which cause high dispersion of oil and spatial variation in oil spill thickness (case of heavy oil and petroleum products). When airborne hyperspectral imagery is processed, it has been found that transparent clouds significantly affect the efficiency of unmixing methods for thin oil spill detection. Their removal, as well as atmospheric correction is strongly recommended. Applying spectral unmixing-based methods on hyperspectral imagery, oil spill detection is effective even in the marginal case of sheens. The results showed that all types and thicknesses of oils can be detected independently of seawater depth through the differences that their spectral signatures present in the wavelengths between 720 μm and 1000 μm. For oil sheens, a single endmember is usually extracted, which leads to relative thickness estimation. For thicker oil spills, many endmembers are extracted each one corresponding to a different thickness and/or emulsion. Further research based on an extended spectral library of measurements in marine environment should be performed in order to enable spectral unmixing-based methods to accurately estimate the oil type, the oil to water ratio of an emulsion as well the oil thickness.