Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow (2024)
- Authors:
- USP affiliated authors: CESAR JUNIOR, ROBERTO MARCONDES - IME ; HAMIDISHAD, NAYEREH - EP
- Unidades: IME; EP
- DOI: 10.1117/1.JRS.18.018502
- Subjects: APRENDIZADO COMPUTACIONAL; PROCESSAMENTO DE IMAGENS; RESERVATÓRIOS
- Keywords: remote sensing imagery; reservoir segmentation; man-made object segmentation; deep learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: San Francisco
- Date published: 2024
- Source:
- Título: Journal of Applied Remote Sensing
- ISSN: 1931-3195
- Volume/Número/Paginação/Ano: v. 18, n. 1, artigo n. 018502, p. 1-20, 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
HAMIDISHAD, Nayereh e CÉSAR JÚNIOR, Roberto Marcondes. Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow. Journal of Applied Remote Sensing, v. 18, n. artigo 018502, p. 1-20, 2024Tradução . . Disponível em: https://doi.org/10.1117/1.JRS.18.018502. Acesso em: 24 fev. 2026. -
APA
Hamidishad, N., & César Júnior, R. M. (2024). Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow. Journal of Applied Remote Sensing, 18( artigo 018502), 1-20. doi:10.1117/1.JRS.18.018502 -
NLM
Hamidishad N, César Júnior RM. Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow [Internet]. Journal of Applied Remote Sensing. 2024 ; 18( artigo 018502): 1-20.[citado 2026 fev. 24 ] Available from: https://doi.org/10.1117/1.JRS.18.018502 -
Vancouver
Hamidishad N, César Júnior RM. Man-made object segmentation around reservoirs by an end-to-end two-phase deep learning-based workflow [Internet]. Journal of Applied Remote Sensing. 2024 ; 18( artigo 018502): 1-20.[citado 2026 fev. 24 ] Available from: https://doi.org/10.1117/1.JRS.18.018502 - Object-based method for identifying new constructions around water reservoirs: preliminary results
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Informações sobre o DOI: 10.1117/1.JRS.18.018502 (Fonte: oaDOI API)
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