Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2019Object based classification of unmanned aerial vehicle (UAV) imagery for forest fires monitoring
Baha Bilgilioglu, B.; Ozturk, Ozan; Sariturk, Batuhan; Seker, Dursun Zafer
In case of fire, determination of burned trees and fire direction is very important. Until now, satellite images and aerial photographs have been widely used in forest fire studies. However, these data can be ineffective in terms of temporal and spatial resolution. In recent years, due to high resolution of provided images, use of Unmanned Aerial Vehicle (UAV) rapidly increased in forest related monitoring studies. Images obtained soon after the forest fires by means of UAVbecome the most important data to evaluate the damage level in the forestry area using different classification techniques. Conventional image classification methods are inefficient for evaluation of high resolution images. However, object-based classification is more accurate than conventional methods. Because, this method uses spectral, neighborhood, texture, hierarchy and size based relationships. In this study, forest fires occurred in Camburnu Natural Park in Surmene District of Trabzon Province located in the Black Sea Region of Turkey was selected as the study area. To determine the destroyed area, high resolution UAV images of the study area were obtained and image pre-processing steps were employed. Object-based classification and pixel-based classification have applied to these images. The boundaries of destroyed forest have been extracted by means of two classification methods. Additionally, combining of these two classification results was investigated to improve the results of the burned area.
- MakaleYalnız künye2018An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery
Catal Reis, Hatice; Bayram, Bülent; Bozkurt, Salih; Ince, Abdulkadir; Demir, Nusret; Seker, Dursun Zafer
This study presents an extended shoreline detection approach from pansharpened images of Turkish RASAT satellite which covers red, green and blue bands of the optical spectrum with 15 m ground resolution and panchromatic band with 7.5 m spatial resolution. The Lake Ercek of Turkey has been selected as the study area, which is a tectonic lake and home to a variety of water birds. The satellite images of the lake taken in 2013 and 2014 were considered for analysis. The proposed shoreline extraction system consists of a sequence of image processing steps in which simple linear clustering (SLIC) and particle swarm optimization (PSO) are the main components. SLIC was used to create superpixels that form basis for object-based image analysis while PSO was employed for classifying objects into corresponding classes. The resulting images still contained unwanted artefacts; therefore, a post-processing step was performed to improve the accuracy of segmentation by applying thresholding, morphological processing, and manual editing for noise removal. The proposed framework was applied on two temporal RASAT images to test the variations of defined parameter settings. The success of the proposed system was to obtain shorelines with satisfying accuracy without using NIR band. Finally, the extracted shorelines were vectorised and compared with manually digitized shorelines from pansharpened satellite images for accuracy assessment.
- MakaleYalnız künye2018Open source library-based 3D face point cloud generation
Bayram, Bulent; Ozkan, Taskin; Reis, Hatice Catal; Bakirman, Tolga; Cetin, Ibrahim; Seker, Dursun Zafer
Three dimensional (3D) face and body modeling is widely used in various fields such as plastic surgery, diagnosis of facial or body anomalies, 3D computer games and 3D simulation software. Since, commercial 3D face and body scanners are usually expensive, an alternative solution with lower cost is highly desirable. The objective of this study is to create 3D facial point cloud using Semi Global Image Matching method with minimum number of images utilizing a cost effective method. A non-metric Canon 600D camera with 18 megapixels resolution (3456 × 5184) and 60 mm macro lens have been used for face imaging that have been taken from a distance of 120 cm. Five faces have been modeled by the developed algorithm and scanned by David SLS-2 structured light system for accuracy assessment. Open source Cloud Compare software has been used for comparing the results of proposed method with the structured light system. The mean accuracy of five faces obtained as 90.5%. It has been observed that illumination conditions, uncontrolled movements of face or body, hair and eyebrow have negative impacts on the obtained results. The sufficiency of Semi global image matching method has been tested to create dense point cloud data from three stereo pairs for 3D facial modelling.
- MakaleYalnız künye2018Automatic extraction of sparse trees from high-resolution ortho-images
Bayram, Bulent; Seker, Dursun Zafer; Jamil, Akhtar; Reis, Hatice Catal; Demir, Nusret; Bozkurt, Salih; Ince, Abdulkadir; Kucuk, Turgay
Obtaining information about tree species distribution in agricultural lands is a topic of interest for various applications, such as tree inventory, forest management, agricultural land management, crop estimation, etc. This information can be derived from images obtained from modern remote sensing technology, which is the most economical way as compare to field surveys covering large geographic areas. Therefore, in this study, a new method is proposed for extraction and counting of sparse and regular distributed individual pistachio trees from agricultural areas on large scale from high-resolution digital ortho-photo maps, which were obtained using an airborne sensor (Ultracam-X). The input images were first smoothed by applying Gaussian filter to reduce the impact of noise. Normalized difference vegetation indices (NDVI) were then derived to obtain vegetation areas followed by Otsu's global thresholding algorithm to obtain candidate tree areas. Further, connected component (CC) analysis was applied to segregate each object. Morphological processing was performed to fill holes within tree objects and get smooth contours, which were obtained by using the Moore-neighbor tracing method (MNTM) for each CC, while geometrical constraints were applied to undermine possible non-tree elements from output image. To further improve the segmentation results for sparse trees, a new method was applied, called quadratic local analysis (QLA). QLA helped to segment the trees, which were missed by the Otsu method due to low contrast and resulted in improved accuracy (3-6%). The obtained results were compared with well-known support vector machine (SVM) classifier. Proposed method produced slightly better results (1-5%) than SVM for extraction of pistachio trees and obtained accuracy for QLA and SVM were 96 and 91% for region 1, while 91 and 90% for region 2 respectively.
- MakaleYalnız künye2017Comparison of low cost 3D structured light scanners for face modeling
Bakirman, Tolga; Gumusay, Mustafa Umit; Reis, Hatice Catal; Selbesoglu, Mahmut Oguz; Yosmaoglu, Serra; Yaras, Mehmet Cem; Seker, Dursun Zafer; Bayram, Bulent
This study aims to compare three different structured light scanner systems to generate accurate 3D human face models. Among these systems, the most dense and expensive one was denoted as the reference and the other two that were low cost and low resolution were compared according to the reference system. One female face and one male face were scanned with three light scanner systems. Point-cloud filtering, mesh generation, and hole-filling steps were carried out using a trial version of commercial software; moreover, the data evaluation process was realized using CloudCompare open-source software. Various filtering and mesh smoothing levels were applied on reference data to compare with other low-cost systems. Thus, the optimum reduction level of reference data was evaluated to continue further processes. The outcome of the presented study shows that low-cost structured light scanners have a great potential for 3D object modeling, including the human face. A considerable cheap structured light system has been used due to its capacity to obtain spatial and morphological information in the case study of 3D human face modeling. This study also discusses the benefits and accuracy of low-cost structured light systems.
- BildiriYalnız künye2017THE ROLE OF NATIONAL AND INTERNATIONAL GEOSPATIAL DATA SOURCES IN COASTAL ZONE MANAGEMENT
Bayram, Bulent; Avsar, Ozgur; Seker, Dursun Zafer; Kayi, Abdullah; Erdogan, Mustafa; Eker, Oktay; Janpaule, Inese; Reis, Hatice Catal
Coastline changes are increasing rapidly due to both natural and human effects. The expansion of touristic, industrial and culture fishing establishments through coastal areas has brought about uncontrolled and unplanned urbanization. In this study, the coastline in Karasu district of Turkey has been extracted by using Unmanned Aerial Vehicle (UAV) images. For this purpose; 7 cm ground sample distance (GSD) UAV images taken in 2013 by Gatewing-X100 by 40% side and 70% forward overlap was used as post images. Produced ortho images were used as the base map for extraction of the coastline. An object-oriented approach has been applied to capture the shoreline from these ortho images. In the presented study, the eCognition object-oriented fuzzy image processing software has been used. This commercial software has the ability to develop custom tools for image classification in addition to its standard object feature tools. Customized arithmetic features have been used to achieve more accurate shoreline segmentation results. Three main object classes have been created as sea, shoreline buffer and land to extract shoreline. The segmentation results were converted into dxf vector data format. The results were compared with manual digitizing of 55 blind readers. An algorithm has been developed by using Matlab to analyze the differences between object-oriented classification and manual digitizing results. Results were evaluated according to students' gender, age, spent time, used software and courses taken. The root mean square error (RMSE) was calculated as 7.15 m.
- BildiriYalnız künye20163D shoreline extraction using orthophoto and LiDAR data
Bayram, Bulent; Demir, Nusret; Ogurlu, Mustafa; Reis, Hatice Catal; Seker, Dursun Zafer
Shorelines are complex ecosystems and vital socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are indispensable for many purposes such as coastline management, agriculture, decision support systems etc. Thus, rapid, up-to-date and accurate information are required to monitor the shorelines. In this study, the Kestel Dam Lake has been selected for evaluation and testing of the methods which will be used for the research project, about shoreline extraction with use of UAV-Based LIDAR data in the region of Terkos-Istanbul, which supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with project nr. 115-718. Kestel Dam is located on the river of Kestel that is constructed for the irrigation purposes, built between 1983 and 1988. Irrigation capacity of dam is about 4077 ha of agricultural areas. The used dataset is LIDAR data which is provided by HGK (Turkish General Command of Mapping). In this study, two different methods are applied with use of LIDAR data, acquired in 2013. One method is mean-shift segmentation and the other is Particle Swarm Optimization Analysis (PSO). The results are both compared with manually created reference vector dataset which is created using orthoimage dataset. The results shows that the developed methods have potential to give success in the research project which will be performed in the region of Terkos which is the study area of the project.
- MakaleYalnız künye2016A semiautomatic segmentation approach to biometric measurement of the talus bone of sedentary women and ballerinas using CT images
Reis, Hatice Catal; Bayram, Bulent; Seker, Dursun Zafer
Background: Ballet produces much stress on bones in the feet of ballerinas. Monitoring and detecting talus bone deformation is important for their quality of health and profession. Objectives: To determine differences in the talus bone between ballerinas and sedentary women. Methods: We evaluated biometric differences in the talus bone of 5 ballerinas referenced to 5 similar sedentary women recruited into the present study. We acquired 20 multidetector computed tomographic images including right and left feet. Semiautomatic region-based image processing using 3D-Doctor (Able Software Corp) was used to create three-dimensional (3D) virtual models of the talus bones. Biometric measurements were made on the 3D models and statistical analysis conducted. Results: The mean talus bone length of ballerinas was 3.37 cm (SD 0.12; range 3.11 to 3.52). The talus bone length of sedentary women was 3.29 cm (SD 0.16; range 3.04 to 3.65), and tended to be shorter than that of the ballerinas (P = 0.08, t test). However, the test was insufficiently powered. The mean volume of the talus bones from the ballerinas was 24.8 cm3 (SD 0.83) and smaller than that from the sedentary women, 26.9 cm3 (SD 1.25) at P < 0.001 (t = 4.38, 18 degrees of freedom. Difference 2.1. Two-tailed 95% confidence interval for difference of means: 1.08 to 3.08). We found less variation in the feet of ballerinas than sedentary women. Conclusions: Volumetric measurements show that feet of ballerinas are smaller and retain similar shape and size than the irregular feet of sedentary women.