2026-04-18T07:26:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/30352025-10-22T11:07:39Zcom_2072_4428com_2072_4427col_2072_487898

00925njm 22002777a 4500 dc Mishra, Puneet author Albano-Gaglio, Michela author Font-i-Furnols, Maria author 2024-04-18 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. 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 A short note on deep contextual spatial and spectral information fusion for hyperspectral image processing: Case of pork belly properties prediction