Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES
4th International Workshop on Geoinformation Science / 4th ISPRS International Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances) -- OCT 14-15, 2017 -- Karabuk Univ, Safranbolu Campus, Safranbolu, TURKEY ; Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of TUBITAK Project (Project No: 115Y718) titled Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example . Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and waterbody classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies. ; Int Soc Photogrammetry & Remote Sensing ; TUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115Y718and] ; This study has been supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with project number 115Y718and entitled Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model -ThreeDimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example''. ; WOS:000568997100022 ; 2-s2.0-85037126131
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES
4th International Workshop on Geoinformation Science / 4th ISPRS International Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling (GeoAdvances) -- OCT 14-15, 2017 -- Karabuk Univ, Safranbolu Campus, Safranbolu, TURKEY ; Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of TUBITAK Project (Project No: 115Y718) titled Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example . Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and waterbody classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies. ; Int Soc Photogrammetry & Remote Sensing ; TUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115Y718and] ; This study has been supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with project number 115Y718and entitled Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model -ThreeDimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example''. ; WOS:000568997100022 ; 2-s2.0-85037126131
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES
Bayram, B. (Autor:in) / Erdem, F. (Autor:in) / Akpinar, B. (Autor:in) / Ince, A. K. (Autor:in) / Bozkurt, S. (Autor:in) / Reis, H. Catal (Autor:in) / Seker, D. Z. (Autor:in)
01.01.2017
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC:
710
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