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Underwater target detection with hyperspectral imagery for search and rescue missions

Fast and precise target detection is an issue in underwater object detection applications such as search and rescue missions. Hyperspectral imaging could be effective in determining different elements in a region thanks to its ability to provide detailed spectral information. Utilization of this rich spectral information calls for the analysis of spectral differences of objects on water surface (dry/wet) and underwater conditions. In our study, we investigated the effect of water column to the target spectrum with two water column correction models and evaluated the results obtained with these models in two detection algorithms. The results show that the methodology enhances underwater object detection performance. The materials used for the study are selected to be typical in search and rescue missions such as metal, cotton fabric with different colors and denim to evaluate the effect of these cases on the detectability of targets for such scenarios.