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Desarrollo de una base de datos de imágenes multiespectrales en visible-infrarrojo cercano para aplicaciones de
Vahid Mohammadi1, Sovi Guillaume Sodjinou1, Pierre Gouton1
1ImViA Laboratory, UFR Sciences et Techniques, Université Bourgogne Europe, 21000 Dijon, France.
Journal of imaging
|January 27, 2026
Resumen
Los investigadores crearon una base de datos gratuita de imágenes multiespectrales con diversas plantas y malezas. Este recurso apoya los avances en demosaicing, segmentación y aprendizaje profundo para tareas de discriminación de cultivos/malezas.
Área de la Ciencia:
- Ciencia Agrícola
- Visión por Computadora
- Procesamiento de Imágenes
Sus antecedentes:
- La imagen multiespectral (MS) se utiliza cada vez más en diversos dominios de investigación.
- Un desafío importante es la escasez de bases de datos de imágenes multiespectrales accesibles debido al desarrollo reciente y la disponibilidad limitada de cámaras MS.
- La creación de bases de datos integrales de imágenes MS es esencial para avanzar en la investigación en este campo.
Objetivo del estudio:
- Establecer una base de datos de imágenes multiespectrales de libre acceso para abordar las limitaciones en la disponibilidad de datos actual.
- Proporcionar imágenes multiespectrales de alta resolución de plantas y malezas, incluidas anotaciones y máscaras, para facilitar la investigación.
- Apoyar el desarrollo y la evaluación de algoritmos para el análisis de imágenes multiespectrales, como demosaicing, segmentación y discriminación de cultivos/malezas.
Principales métodos:
- Se utilizaron dos cámaras MS de alta gama (visible e infrarrojo cercano) basadas en tecnología de matriz de filtros de la plataforma PImRob de la Universidad de Borgoña.
- Se adquirió y curó un conjunto de datos de imágenes MS de alta resolución.
- Se proporcionaron imágenes originales en bruto y demosaiced, junto con imágenes anotadas y máscaras de segmentación.
Principales resultados:
- Desarrollada y publicada una base de datos de imágenes multiespectrales de libre acceso.
- La base de datos contiene diversas imágenes multiespectrales de plantas y malezas con anotaciones y máscaras detalladas.
- Se incluyeron datos de imágenes tanto en bruto como procesados (demosaiced).
Conclusiones:
- La base de datos MS establecida sirve como un recurso valioso para la comunidad científica.
- Es particularmente beneficioso para la investigación centrada en técnicas de demosaicing, algoritmos de segmentación y aplicaciones de aprendizaje profundo en agricultura.
- La base de datos tiene como objetivo acelerar el progreso en la identificación automatizada de cultivos y malezas utilizando imágenes multiespectrales.
Palabras clave:
base de datos de imágenes anotadasaprendizaje profundodemosaicingmatriz de filtros multiespectralessegmentaciónMás Videos Relacionados
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