Automated identification of river hydromorphological features using UAV high resolution aerial imagery

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dc.contributor.authorRivas Casado, Monica
dc.contributor.authorBallesteros Gonzalez, Rocio
dc.contributor.authorKriechbaumer, Thomas
dc.contributor.authorVeal, Amanda Veal
dc.date.accessioned2016-03-10T16:05:07Z
dc.date.available2016-03-10T16:05:07Z
dc.date.issued2015-11-04
dc.description.abstractEuropean legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.en_UK
dc.identifier.citationMonica Rivas Casado, Rocio Ballesteros Gonzalez, Thomas Kriechbaumer and Amanda Veal, Automated identification of river hydromorphological features using UAV high resolution aerial imagery. Sensors, 2015, Vol.15(11), pp27969-27989en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttp://dx.doi.org/10.3390/s151127969
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/9780
dc.language.isoenen_UK
dc.publisherMDIPen_UK
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectUnmanned Aerial Vehicleen_UK
dc.subjectphotogrammetryen_UK
dc.subjectArtificial Neural Networken_UK
dc.subjectfeature recognitionen_UK
dc.subjecthydromorphologyen_UK
dc.titleAutomated identification of river hydromorphological features using UAV high resolution aerial imageryen_UK
dc.typeArticleen_UK

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