Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

2019 ◽  
Vol 42 (6) ◽  
pp. e13225 ◽  
Author(s):  
Joshua H. Aheto ◽  
Xingyi Huang ◽  
Xiaoyu Tian ◽  
Yi Ren ◽  
Ernest Bonah ◽  
...  
Keyword(s):  
Lipid Oxidation ◽  
Hyperspectral Imaging ◽  
Pork Meat ◽  
Imaging Data ◽  
Image Information ◽  
Fast Prediction

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

Calibration of Hyperspectral Imaging Data: VIRTIS-M Onboard Venus Express

2010 ◽  
Author(s):  
Alejandro Cardesin Moinelo ◽  
Giuseppe Piccioni ◽  
Eleonora Ammannito ◽  
Gianrico Filacchione ◽  
Pierre Drossart
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Data ◽  
Venus Express

Robust Blind Image Fusion for Misaligned Hyperspectral Imaging Data

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Leon Bungert ◽  
Matthias J. Ehrhardt ◽  
Rafael Reisenhofer
Keyword(s):  
Image Fusion ◽  
Hyperspectral Imaging ◽  
Imaging Data ◽  
Blind Image

Effects of Pork Meat Cut and Packaging Type on Lipid Oxidation and Oxidative Products during Refrigerated Storage (8 °C)

2008 ◽  
Vol 73 (3) ◽  
pp. C127-C134 ◽  
Author(s):  
S.Y. Park ◽  
Y.J. Kim ◽  
H.C. Lee ◽  
S.S. Yoo ◽  
J.H. Shim ◽  
...  
Keyword(s):  
Lipid Oxidation ◽  
Refrigerated Storage ◽  
Pork Meat ◽  
Oxidative Products

scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

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