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dc.contributor.authorBayram, Bulent
dc.contributor.authorSeker, Dursun Zafer
dc.contributor.authorJamil, Akhtar
dc.contributor.authorReis, Hatice Catal
dc.contributor.authorDemir, Nusret
dc.contributor.authorBozkurt, Salih
dc.contributor.authorKucuk, Turgay
dc.date.accessioned2021-11-09T19:49:46Z
dc.date.available2021-11-09T19:49:46Z
dc.date.issued2018
dc.identifier.issn1866-7511
dc.identifier.issn1866-7538
dc.identifier.urihttps://doi.org/10.1007/s12517-018-3680-6
dc.identifier.urihttps://hdl.handle.net/20.500.12440/4121
dc.description.abstractObtaining 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.en_US
dc.description.sponsorshipTEYDEP Project entitled Development of Object Based Neural Network Image Processing System Determination of Vegetation and Forestry Boundaries [7140512]en_US
dc.description.sponsorshipThis study is a part of TEYDEP Project entitled Development of Object Based Neural Network Image Processing System Determination of Vegetation and Forestry Boundaries (Project Nr. 7140512). It was supervised by EMI Group-Turkey and consulted by Prof. Dr. Bulent Bayram.en_US
dc.language.isoengen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal of Geosciencesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutomatic digitizationen_US
dc.subjectSparse tree extractionen_US
dc.subjectOtsu methoden_US
dc.subjectMoore-neighbor tracing methoden_US
dc.subjectConnect component analysisen_US
dc.subjectMorphological processingen_US
dc.subjectSupport vector machineen_US
dc.titleAutomatic extraction of sparse trees from high-resolution ortho-imagesen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wospublicationidWOS:000436082400008en_US
dc.description.scopuspublicationid2-s2.0-85049873927en_US
dc.departmentGümüşhane Üniversitesien_US
dc.authoridDemir, Nusret / 0000-0002-8756-7127
dc.authoridSeker, Dursun Zafer / 0000-0001-7498-1540
dc.authoridJAMIL, AKHTAR / 0000-0002-2592-1039
dc.identifier.volume11en_US
dc.identifier.issue12en_US
dc.identifier.doi10.1007/s12517-018-3680-6
dc.authorwosidBayram, Bulent / J-2002-2015
dc.authorwosidDemir, Nusret / B-7778-2016
dc.authorwosidSeker, Dursun Zafer / ABA-7384-2020
dc.authorwosidJAMIL, AKHTAR / M-6215-2019
dc.authorscopusid15130508500
dc.authorscopusid6602931561
dc.authorscopusid49863650600
dc.authorscopusid57192666861
dc.authorscopusid56844945100
dc.authorscopusid57198798066
dc.authorscopusid57198801914


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