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10 October 2018 Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps
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Abstract
Unmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed, automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood monitoring and prevention strategies are required. However, the limited battery life of small lightweight UAVs imposes efficient strategies to subsample the sensing field in this context. This paper proposes a novel solution to maximise the number of inspected flooded surface while keeping the travelled distance bounded. Our proposal solves the so-called continuous Travelling Salesman Problem (TSP), where the costs of travelling from one location to another depend not only on the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered flooded surface approximately 3.33 times greater for the same travelled distance when compared to the conventional TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to monitor water resources.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pablo Casaseca-de-la-Higuera, Antonio Tristán-Vega, Carlos Hoyos-Barceló, Susana Merino-Caviedes, Qi Wang, Chunbo Luo, Xinheng Wang, and Zhi Wang "Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps", Proc. SPIE 10784, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, 107840D (10 October 2018); https://doi.org/10.1117/12.2325645
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