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The rapid and accurate detection and identification of water pollutants play an enormous role is preserving water ecosystems and protecting the surrounding environment. Such detection and identification are usually conducted in situ, using traditional measurement methods. These methods however are very time consuming and costly especially when conducting them on a large scale. A team of specialists from the Department of Remote Sensing and Photogrammetry from the Military University of Technology in Warsaw have been taking part in a project entitled “IRAMSWater – Innovative remote sensing system for the monitoring of pollutants in rivers, offshore waters and flooded areas” (PBS1/B9/8/2012) financed by the polish National Centre for Research and Development. Its main aim is the creation of a remote sensing system based on hyperspectral sensors which will enable the evaluation, detection and distribution of biological, physical and chemical pollutants in the examined waters in real time. These analyses will be conducted based on spectral characteristics of a wide selection of pollutants. In order to acquire these spectral characteristics in a precise manner from hyperspectral data, the research team was required to establish a precise methodology for obtaining these data. The most common approach is to acquire the imagery first, ensuring that each scene contains at least one reference panel with a well know spectral characteristic, and then transforming the imagery and calculating the spectral response curves during post-processing. The IRAMSWater team had proposed a method of extracting precise reflectance coefficients in quasi real time without the need for using a reference panel on the scene. This is done by precisely determining the camera exposure parameters in laboratory conditions. The article presents laboratory experiments conducted using two XEVA video sensors (400–1700 nm spectral data registration), which allowed the authors to form a methodology for acquiring spectral reflectance coefficients of objects within a scene without the need for a reference panel.

Using XEVA video sensors in acquiring spectral reflectance coefficients