Sürdürülebilir Kalkınma Amaçları
Yayınlar
- 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 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.
- 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.