Yazarlar

• Karas, I.R.(2)
• Akpinar, B.(1)
• Ane, B.K.(1)
• Bayram, B.(1)
• Ben Ahmed, M.(1)
• Boudhir, A.A.(1)
• Bozkurt, S.(1)
• Catal Reis, H.(1)
• Erdem, F.(1)
• Gunen, M.A.(1)
• Ince, A.K.(1)
• Isikdag, U.(1)
• Rahman, A.A.(1)
• Seker, D.Z.(1)

• BildiriAçık Erişim2021/r/gumushane/14840
  QUALITY ASSESSMENT of PANSHARPENED RASAT and LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

  Gunen, M.A.; Karas, I.R.; Ben Ahmed, M.; Boudhir, A.A.; Ane, B.K.

  Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.

  İncele
• BildiriAçık Erişim2017/r/gumushane/15165
  THE EFFICIENCY of RANDOM FOREST METHOD for SHORELINE EXTRACTION from LANDSAT-8 and GOKTURK-2 IMAGERIES

  Bayram, B.; Erdem, F.; Akpinar, B.; Ince, A.K.; Bozkurt, S.; Catal Reis, H.; Seker, D.Z.; Karas, I.R.; Rahman, A.A.; Isikdag, U.

  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.

  İncele