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In this work, a new thermograms quantitative post-processing method for the optimization of hydrocarbons monitoring on sea surface is presented. The proposed method is based on mathematically filtering and integrating discrete sets of thermal images, acquired during hydrocarbons flowing into the sea from dynamic sources (i.e. boats). It permits to avoid some typical error causes and to detect smaller quantities of pollutant with respect to actual qualitative technique capability. Moreover, the monitoring process automation is implemented, in order to develop a live method, able to notify and to alarm if a too high hydrocarbons level is reached in the area of interest. At the present, the monitoring of pollutant presence on the sea surface is typically carried out by means of some standard methods, among which the on-the-ground water sampling and the visual survey are some examples. Both above mentioned methods show some limits, due to the inability of analyzing wide areas and to the difficulty of detecting small pollution sources. Furthermore, they don’t permit to implement an automated method [1]. In last years, several studies have been carried out in order to implement wide areas pollution control techniques, thus the use of qualitative infrared thermography (IT) to monitor the hydrocarbons marine injection has been increased noticeably. Some works [2-3] have developed qualitative methods, showing the technique capability of detecting pollutant pouring on the sea, and demonstrating that the ability of identifying contaminant is due to the different emissivity values of water (ewater=0.98) and gasoline (egasoline=0.90). However, the IR thermography pollutant on the sea surface detection is strongly affected by a wide number of variables, that might result in misleading results and, in some case, in the impossibility of performing the analysis. Weather conditions are key factors in the technique implementation, as example the presence of rain can lead to inhomogeneity in thermal image, caused by both the rain - water temperature difference and the sea surface ripples, thus hiding the pollutant presence effect. In addition, also wind and moisture are critical issues for the test results. Other examples of noise source are the wave motion (which is the most important test limiting factor in some conditions), the presence of moving subjects, such as boats or birds. Furthermore, the technique capability is affected by geometrical feature of pollutant film, such as size and thickness [4]. The bigger and thicker the hydrocarbon injection, the better its identification. In this paper, a new thermograms post-processing method which allows to avoid some of the limitations associated with the existing IT technique is introduced. The proposed method concerns on the acquisition of thermograms at different times after the transit of a pollutant source and on automated algorithms that alter these images in order to avoid noise and to provide a better thermal contrast between polluted areas and surrounding zones. The basic theoretical idea is that as an hydrocarbon film is present on the water surface, it tends to stay compact over time, avoiding dispersions. This stability condition can be exploited by image integration to improve the pollutant identification, regardless of surrounding conditions. Both laboratory and on-field experimental measurements have been carried out in order to analyze different condition, thus defining dynamic integration intervals, updated depending on weather, which allow to minimize the effect of external noise sources. The main aim of this work is the exploiting of dynamic image integration to implement an automatic system for the hydrocarbons on the sea detection and tracking, avoiding several noise source, which are limiting factors of the technique.

Dynamic thermograms processing for the optimization and the automation of hydrocarbons pollution on sea surface detection