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Sensors 2017, 17(3), 446; doi:10.3390/s17030446

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Department of Control Engineering and Industrial Informatics, University Politehnica of Bucharest, Bucharest 060042, Romania
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Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 28 December 2016 / Accepted: 20 February 2017 / Published: 23 February 2017
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Abstract

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. View Full-Text
Keywords: unmanned aerial vehicle; path planning; flood detection; feature selection; image processing; image segmentation; texture analysis unmanned aerial vehicle; path planning; flood detection; feature selection; image processing; image segmentation; texture analysis
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Popescu, D.; Ichim, L.; Stoican, F. Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing. Sensors 2017, 17, 446.

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