2026-04-17T20:32:55Zhttps://recercat.cat/oai/requestoai:recercat.cat:20.500.12327/30352025-10-22T11:07:39Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT
author
Mishra, Puneet
author
Albano-Gaglio, Michela
author
Font-i-Furnols, Maria
other
Indústries Alimentàries
group
Qualitat i Tecnologia Alimentària
2025-10-22T11:07:39Z
2025-10-22T11:07:39Z
2024-04-18
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.
0886-9383
http://hdl.handle.net/20.500.12327/3035
https://doi.org/10.1002/cem.3552
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.
eng
Attribution 4.0 International
A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction
info:eu-repo/semantics/article