This paper presents a hyperspectral camera system and data processing workflow designed to remotely detect, identify, and quantify \(SO_2\) emissions from ships in real-time, determining their fuel sulfur content (FSC). This technology is intended to assist maritime authorities in enforcement of the maritime sulfur emissions regulations. The focus of the study is on the automatic detection of ships and their exhaust plumes, enabling a fully automated verification of FSC. The system employs classic motion detection techniques, such as frame differencing and traditional computer vision morphological operations, to identify a ship, the plume and the chimney in a scene. A spectral angle mapper is the main method for finding segments in the hyperspectral data cubes. These simple methods can lead to a robust detection of the relevant scene pixels and calculation of the FSC from the spectra of these pixels.