Integrating object-based and pixel-based segmentation for building footprint extraction from satellite images

Basit Öğe Kaydı
dc.authoridWazirali, Raniyah/0000-0002-3609-9351
dc.authoridAlshwaiyat, Rami/0000-0003-3913-6397
dc.authoridTASOGLU, Enes/0000-0002-6365-6926
dc.contributor.authorAbujayyab, Sohaib K. M.
dc.contributor.authorAlmajalid, Rania
dc.contributor.authorWazirali, Raniyah
dc.contributor.authorAhmad, Rami
dc.contributor.authorTasoglu, Enes
dc.contributor.authorKaras, Ismail R.
dc.contributor.authorHijazi, Ihab
dc.date.accessioned2024-09-29T15:57:40Z
dc.date.available2024-09-29T15:57:40Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAccurately delineating building footprints from optical satellite imagery presents a formidable challenge, particularly in urban settings characterized by intricate and diverse structures. Consequently, enhancing the utility of these images for geospatial data updates demands meticulous refinement. Machine learning algorithms have made notable contributions in this context, yet the pursuit of precision remains an ongoing challenge. This paper aims to enhance the accuracy of building footprint extraction through the integration of object-based and pixel-based segmentation techniques. Additionally, it evaluates the performance of machine learning methodologies, specifically LightGBM, XGBoost, and Neural Network (NN) approaches. The model's evaluation employed low spectral resolution optical images, widely accessible and cost-effective for acquisition. The study's outcomes demonstrate a substantial enhancement in extraction accuracy compared to extant literature. The proposed methodology attains an overall accuracy of 99.39%, an F1 measurement of 0.9935, and a Cohen Kappa index of 0.9870. Thus, the proposed approach signifies a noteworthy advancement over existing techniques for building footprint extraction from high-resolution optical imagery.en_US
dc.identifier.doi10.1016/j.jksuci.2023.101802
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85178217958en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2023.101802
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4926
dc.identifier.volume35en_US
dc.identifier.wosWOS:001108518900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBuilding footprint extractionen_US
dc.subjectObject-based segmentationen_US
dc.subjectPixel-based segmentationen_US
dc.subjectMachine learningen_US
dc.subjectSatellite imagesen_US
dc.titleIntegrating object-based and pixel-based segmentation for building footprint extraction from satellite imagesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu