Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps
Casaseca-de-la-Higuera, P; Tristán Vega, A; Hoyos-Barcelo, C; et al.Merino-Caviedes, S; Wang, Q; Luo, C; Wang, X; Wang, Z
Date: 10 October 2018
Publisher
Society of Photo-optical Instrumentation Engineers (SPIE)
Publisher DOI
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 ...
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. The limited battery life of small lightweight UAVs imposes
efficient strategies to subsample the sensing field. This paper proposes a novel solution to maximise the number of
inspected flooded cells while keeping the travelled distance bounded. Our proposal solves the so-called continuous
Travelling Salesman Problem (TSP), where the costs of travelling from one cell 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 4 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
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0