ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 141-145, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-141-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Nov 2017
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES
B. Bayram1, F. Erdem2, B. Akpinar1, A. K. Ince2, S. Bozkurt2, H. Catal Reis3, and D. Z. Seker4 1Yildiz Technical University, Civil Engineering Faculty, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, Turkey
2Yildiz Technical University, Graduate School of Natural And Applied Sciences, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, Turkey
3Gumushane University, Faculty of Engineering, Dept. of Geomatics, Gumushane, Turkey
4ITU, Civil Engineering Faculty, 80626 Maslak Istanbul, Turkey
Keywords: Random Forest, Shoreline Extraction, Image segmentation, LANDSAT-8, GOKTURK-2 Abstract. 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 water-body 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.
Conference paper (PDF, 923 KB)


Citation: Bayram, B., Erdem, F., Akpinar, B., Ince, A. K., Bozkurt, S., Catal Reis, H., and Seker, D. Z.: THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 141-145, https://doi.org/10.5194/isprs-annals-IV-4-W4-141-2017, 2017.

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