THE EFFICIENCY of RANDOM FOREST METHOD for SHORELINE EXTRACTION from LANDSAT-8 and GOKTURK-2 IMAGERIES

B. Bayram*, F. Erdem, B. Akpinar, A. K. Ince, S. Bozkurt, H. Catal Reis, D. Z. Seker

*Bu çalışma için yazışmadan sorumlu yazar

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16 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)141-145
Sayfa sayısı5
DergiISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Hacim4
Basın numarası4W4
DOI'lar
Yayın durumuYayınlandı - 15 Kas 2017
Etkinlik4th International GeoAdvances Workshop - GeoAdvances 2017: ISPRS Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling - Safranbolu, Karabuk, Turkey
Süre: 14 Eki 201715 Eki 2017

Bibliyografik not

Publisher Copyright:
© Authors 2017.

Finansman

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 – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example “.

FinansörlerFinansör numarası
TUBITAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

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