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

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 3; p. 446
• Main Authors: Popescu, Dan, Ichim, Loretta, Stoican, Florin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23-02-2017; MDPI
• Subjects: Damage assessment; feature selection; Flood damage; flood detection; Floods; Ground based control; Image acquisition; Image processing; image segmentation; Natural disasters; Neural networks; Nodes; path planning; Spatial data; texture analysis; unmanned aerial vehicle; Unmanned aerial vehicles; image processing; unmanned aerial vehicle; image segmentation; path planning; flood detection; feature selection; texture analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17030446

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.