Comparing the Segment Anything Model with Region Growing Algorithms in the detection of irrigated croplands

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorNational Institute for Space Research (INPE)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPetrone, Felipe Gomes-
Autor(es): dc.creatorDa Silva, Darlan Teles-
Autor(es): dc.creatorMaia, Aluizio Brito-
Autor(es): dc.creatorSanches, Ieda Del'Arco-
Autor(es): dc.creatorDantas Chaves, Michel Eustáquio-
Autor(es): dc.creatorGarcia Fonseca, Leila Maria-
Autor(es): dc.creatorKörting, Thales Sehn-
Autor(es): dc.creatorAdami, Marcos-
Data de aceite: dc.date.accessioned2025-08-21T19:57:02Z-
Data de disponibilização: dc.date.available2025-08-21T19:57:02Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.14393/rbcv76n0a-72592-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306011-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306011-
Descrição: dc.descriptionThe advance of remote sensing and geotechnologies has helped to solve agricultural-related problems, especially those connected to management practices such as irrigation. Image segmentation techniques, for example, bring the possibility of identifying areas and borders of irrigated croplands,a factor that can enhance monitoring and yield estimates. In this research field, a recent innovation is the Segment Anything Model (SAM) algorithm. Thus, this study aimed to compare SAM with two well-known remote sensing image segmentation algorithms, Region Growing and Baatz-Schape, in order to delineate irrigated agricultural lands in the Brazilian semiarid region. The findings indicate that SAM has the potential to generate homogeneous segments when examining such lands. However, it requires refinements in order to distinguish fields with varying crops and to improve the high computational cost of SAM, especially for big data. Additionally, the choice and testing of parameters are crucial for the optimal performance of segmentation algorithms.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionNational Institute for Space Research (INPE), SP-
Descrição: dc.descriptionSão Paulo State University (UNESP), SP-
Descrição: dc.descriptionSão Paulo State University (UNESP), SP-
Descrição: dc.descriptionFAPESP: N° 2021/07382-2-
Idioma: dc.languageen-
Relação: dc.relationRevista Brasileira de Cartografia-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectImage Segmentation-
Palavras-chave: dc.subjectIrrigated Croplands-
Palavras-chave: dc.subjectRemote Sensing Images-
Título: dc.titleComparing the Segment Anything Model with Region Growing Algorithms in the detection of irrigated croplands-
Título: dc.titleComparando o Segment Anything Model com Algoritmos de Crescimento de Regiões na detecção de áreas irrigáveis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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