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Hyperspectral images (HSI) have been extensively applied in a multitude of domains, due to their combined spatial and spectral characteristics along with a wealth of spectral bands. The ingenious combination of spatial and spectral information in HSI classification has remained a central research area for an extended period. In the classification process, it is essential to choose an expanded neighborhood window for learning.
View Article and Find Full Text PDFDeep Learning and Hyperspectral Imaging for Liver Cancer Staging and Cirrhosis Differentiation.
J Biophotonics
January 2025
Nanjing University of Chinese Medicine, Nanjing, China.
Liver malignancies, particularly hepatocellular carcinoma (HCC), pose a formidable global health challenge. Conventional diagnostic techniques frequently fall short in precision, especially at advanced HCC stages. In response, we have developed a novel diagnostic strategy that integrates hyperspectral imaging with deep learning.
View Article and Find Full Text PDFHarnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach.
Plant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFVis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning.
Foods
January 2025
CREA-Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy.
The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered.
View Article and Find Full Text PDFCooking loss estimation of semispinalis capitis muscle of pork butt using a deep neural network on hyperspectral data.
Meat Sci
January 2025
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea. Electronic address:
This study evaluated the performance of a deep-learning-based model that predicted cooking loss in the semispinalis capitis (SC) muscle of pork butts using hyperspectral images captured 24 h postmortem. To overcome low-scale samples, 70 pork butts were used with pixel-based data augmentation. Principal component regression (PCR) and partial least squares regression (PLSR) models for predicting cooking loss in SC muscle showed higher R values with multiplicative signal correction, while the first derivative resulted in a lower root mean square error (RMSE).
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