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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)141-145
Number of pages5
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number4W4
DOIs
Publication statusPublished - 15 Nov 2017
Event4th International GeoAdvances Workshop - GeoAdvances 2017: ISPRS Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling - Safranbolu, Karabuk, Turkey
Duration: 14 Oct 201715 Oct 2017

Bibliographical note

Publisher Copyright:
© Authors 2017.

Funding

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 “.

FundersFunder number
TUBITAK
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • GOKTURK-2
    • Image segmentation
    • LANDSAT-8
    • Random Forest
    • Shoreline Extraction

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