Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images.
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| Title: | Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images. |
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| Authors: | Shoaib A; Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia., Nadeem MW; Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia., Sariff N; School of Engineering, Faculty of Innovation and Technology, Taylor's University, Malaysia., Haider ST; Department of Electronics Engineering, Faculty of Electronics and Electrical Engineering, University of Engineering and Technology, Taxila, Pakistan., Ullah F; Department of Computing, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia., Rehman AU; Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India.; Applied Science Research Center, Applied Science Private University, Amman, Jordan., Muhammad S; School of Computing, Gachon University, Seongnam-si, Republic of Korea., Nawaz R; School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom., Khan MA; Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, Pakistan.; Jadara University Research Center, Jadara University, Irbid, Jordan.; Center of Excellence in Precision Medicine and Digital Health, Department of Physicology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand. |
| Source: | PloS one [PLoS One] 2026 May 15; Vol. 21 (5), pp. e0348364. Date of Electronic Publication: 2026 May 15 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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