A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction
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Fecha de publicación
2024-04-18ISSN
0886-9383
Resumen
This study demonstrates a new approach to process hyperspectral images
where both the contextual spatial information as well as the spectral
information are used to predict sample properties. The deep contextual spatial
information is extracted using the deep feature extraction from pretrained
resnet-18 deep learning architecture, while the spectral information was
readily available as the average pixel values. To fuse the information in a
complementary way, a multiblock modeling approach called sequential
orthogonalized partial least squares was used. The sequential model guarantees
that the information learned is complementary from spatial and spectral
domains. The potential of the approach is demonstrated to predict several
physical and chemical properties in pork bellies.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
English
Materias (CDU)
663/664 - Alimentos y nutrición. Enología. Aceites. Grasas
Páginas
9
Publicado por
Wiley
Publicado en
Journal of Chemometrics
Citación
Mishra, Puneet, Michela Albano‐Gaglio, and Maria Font‐i‐Furnols. 2024. “A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction”. Journal of Chemometrics, April. doi:10.1002/cem.3552.
Número del acuerdo de la subvención
MICIU/Programa Estatal de I+D+I orientada a los retos de la sociedad/RTI2018-096993-B-I00/ES/CLASIFICACION Y EVALUACION DE LA CALIDAD GLOBAL DE LA PANCETA DE CERDO MEDIANTE TECNOLOGIAS NO DESTRUCTIVAS Y PERCEPCION POR PARTE DE LOS CONSUMIDORES/
Program
Qualitat i Tecnologia Alimentària
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