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.authoridTASOGLU, Enes/0000-0002-6365-6926
dc.authoridAlshwaiyat, Rami/0000-0003-3913-6397
dc.authoridALMAJALID, RANIA/0000-0001-9930-3957
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.accessioned2024-11-07T13:35:02Z
dc.date.available2024-11-07T13:35:02Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
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.
dc.identifier.doi10.1016/j.jksuci.2023.101802
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85178217958
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2023.101802
dc.identifier.urihttps://hdl.handle.net/11480/16298
dc.identifier.volume35
dc.identifier.wosWOS:001108518900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectBuilding footprint extraction
dc.subjectObject-based segmentation
dc.subjectPixel-based segmentation
dc.subjectMachine learning
dc.subjectSatellite images
dc.titleIntegrating object-based and pixel-based segmentation for building footprint extraction from satellite images
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu