SEARCH RESULT

The main objective of this study is to automatically extract tea gardens from large geographic areas using high-resolution digital orthophoto maps. To achieve this objective, object-based image analysis and decision tree (DT) classifier were integrated. For segmentation, multi-resolution image segmentation algorithm was used which is implemented in Definiens Developer commercial software. Both scale and compactness parameters were empirically calculated that produced optimal results. The segmented objects were selected manually for training the DT classifier from all used images. Spectral and textural features were extracted from each segment and to make the features robust against local variations, they were extracted at two image scales and final feature vector was formed by averaging the two feature vectors. The selected optimal features were used to train the DT classifier and then applied it on the test data to generate thematic maps for tea gardens. The performance of the proposed method was evaluated by comparing results with the reference data that produced promising results for mapping tea gardens (overall accuracy 88%) on our dataset.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Akhtar Jamil B. BAYRAM

The coastal ecosystems are very sensitive to external influences. Coastal resources such as sand dunes, coral reefs and mangroves has vital importance to prevent coastal erosion. Human based effects also threats the coastal areas. Therefore, the change of coastal areas should be monitored. Up-todate, accurate shoreline information is indispensable for coastal managers and decision makers. Remote sensing and image processing techniques give a big opportunity to obtain reliable shoreline information. In the presented study, NIR bands of seven 1:5000 scaled digital orthophoto images of Riga Bay-Latvia have been used. The Object-oriented Simple Linear Clustering method has been utilized to extract shoreline of Riga Bay. Bend and Douglas-Peucker methods have been used to simplify the extracted shoreline to test the effect of both methods. Photogrammetrically digitized shoreline has been taken as reference data to compare obtained results. The accuracy assessment has been realised by Digital Shoreline Analysis tool. As a result, the achieved shoreline by the Bend method has been found closer to the extracted shoreline with Simple Linear Clustering method.

International Workshop on GeoInformation Science
GEOADVANCES

B. BAYRAM A. Sen M.O. Selbesoglu I. Vārna P. Petersons N.O. Aykut D. Z. Seker

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.

International Workshop on GeoInformation Science
GEOADVANCES

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