Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorReis, Hatice Catal
dc.contributor.authorBayram, Bulent
dc.contributor.authorBozkurt, Salih
dc.contributor.authorInce, Abdulkadir
dc.contributor.authorDemir, Nusret
dc.contributor.authorSeker, Dursun Zafer
dc.date.accessioned2021-11-09T19:49:32Z
dc.date.available2021-11-09T19:49:32Z
dc.date.issued2018
dc.identifier.issn0255-660X
dc.identifier.issn0974-3006
dc.identifier.urihttps://doi.org/10.1007/s12524-018-0804-0
dc.identifier.urihttps://hdl.handle.net/20.500.12440/4064
dc.description.abstractThis 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.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [115Y718]en_US
dc.description.sponsorshipThis study presents the preliminary results of the algorithms which will also be used in the project entitled Automatic 3D Shoreline Extraction and Analysis from UAV and UAV-LIDAR Data for Sustainable Shoreline Monitoring: Case Study of Terkos (Istanbul)'' which is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with Project Number 115Y718.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of The Indian Society of Remote Sensingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectSimple linear clusteringen_US
dc.subjectImage processingen_US
dc.subjectShoreline extractionen_US
dc.titleAn Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imageryen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wospublicationidWOS:000443028500004en_US
dc.description.scopuspublicationid2-s2.0-85050205168en_US
dc.departmentGümüşhane Üniversitesien_US
dc.authoridSeker, Dursun Zafer / 0000-0001-7498-1540
dc.authoridDemir, Nusret / 0000-0002-8756-7127
dc.identifier.volume46en_US
dc.identifier.issue8en_US
dc.identifier.startpage1223en_US
dc.identifier.doi10.1007/s12524-018-0804-0
dc.identifier.endpage1232en_US
dc.authorwosidBayram, Bulent / J-2002-2015
dc.authorwosidSeker, Dursun Zafer / ABA-7384-2020
dc.authorwosidDemir, Nusret / B-7778-2016
dc.authorscopusid57192666861
dc.authorscopusid15130508500
dc.authorscopusid57198798066
dc.authorscopusid57198801914
dc.authorscopusid56844945100
dc.authorscopusid6602931561


Bu öğenin dosyaları:

DosyalarBoyutBiçimGöster

Bu öğe ile ilişkili dosya yok.

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster