Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing
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 pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/3/446 |
| _version_ | 1798034247028572160 |
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| author | Dan Popescu Loretta Ichim Florin Stoican |
| author_facet | Dan Popescu Loretta Ichim Florin Stoican |
| author_sort | Dan Popescu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:41:43Z |
| format | Article |
| id | doaj.art-74a4e1bfe235418dba75dfd382f51da9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:41:43Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-74a4e1bfe235418dba75dfd382f51da92022-12-22T04:04:11ZengMDPI AGSensors1424-82202017-02-0117344610.3390/s17030446s17030446Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image ProcessingDan Popescu0Loretta Ichim1Florin Stoican2Department of Control Engineering and Industrial Informatics, University Politehnica of Bucharest, Bucharest 060042, RomaniaDepartment of Control Engineering and Industrial Informatics, University Politehnica of Bucharest, Bucharest 060042, RomaniaDepartment of Control Engineering and Industrial Informatics, University Politehnica of Bucharest, Bucharest 060042, RomaniaFloods 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.http://www.mdpi.com/1424-8220/17/3/446unmanned aerial vehiclepath planningflood detectionfeature selectionimage processingimage segmentationtexture analysis |
| spellingShingle | Dan Popescu Loretta Ichim Florin Stoican Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing Sensors unmanned aerial vehicle path planning flood detection feature selection image processing image segmentation texture analysis |
| title | Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing |
| title_full | Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing |
| title_fullStr | Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing |
| title_full_unstemmed | Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing |
| title_short | Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing |
| title_sort | unmanned aerial vehicle systems for remote estimation of flooded areas based on complex image processing |
| topic | unmanned aerial vehicle path planning flood detection feature selection image processing image segmentation texture analysis |
| url | http://www.mdpi.com/1424-8220/17/3/446 |
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