PS8-9 Classification of Splanchnic Tissue using Near-infrared Hyperspectral Imaging Data(PS8: Poster Short Presentation VIII,Poster Session)

2015 ◽  
Vol 2015.8 (0) ◽  
pp. 361
Author(s):  
Mai Kasai ◽  
Takumi Ishikawa ◽  
Yuya Yasuda ◽  
Hiroshi Takemura ◽  
Hiroshi Mizoguchi ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Poster Session ◽  
Near Infrared ◽  
Imaging Data ◽  
Short Presentation

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.

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

2019 ◽  
Vol 296 ◽  
pp. 126630 ◽  
Author(s):  
Pengcheng Nie ◽  
Jinnuo Zhang ◽  
Xuping Feng ◽  
Chenliang Yu ◽  
Yong He
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging Technology ◽  
Hybrid Seeds

Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

2016 ◽  
Vol 8 (48) ◽  
pp. 8498-8505 ◽  
Author(s):  
Sófacles Figueredo Carreiro Soares ◽  
Everaldo Paulo Medeiros ◽  
Celio Pasquini ◽  
Camilo de Lelis Morello ◽  
Roberto Kawakami Harrop Galvão ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
New Strategy ◽  
Cotton Seeds ◽  
Non Destructive

This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety.

Near-infrared hyperspectral imaging for grading and classification of pork

Meat Science ◽  
2012 ◽  
Vol 90 (1) ◽  
pp. 259-268 ◽  
Author(s):  
Douglas Barbin ◽  
Gamal Elmasry ◽  
Da-Wen Sun ◽  
Paul Allen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared

CHEMOMETRIC STRATEGIES FOR NEAR INFRARED HYPERSPECTRAL IMAGING ANALYSIS: CLASSIFICATION OF SEED GENOTYPES

2021 ◽  
Author(s):  
Simone Simões ◽  
Priscilla Rocha ◽  
Everaldo Paulo Medeiros ◽  
Carolina Silva
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Images ◽  
Imaging Analysis

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based in HSI in the...

<i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>

2017 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xuan Chu ◽  
Hongzhe Jiang ◽  
Beibei Jia ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Classification ◽  
Maize Kernels
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