Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm

2020 ◽  
Vol 109 ◽  
pp. 103418 ◽  
Author(s):  
Quan Zhou ◽  
Wenqian Huang ◽  
Shuxiang Fan ◽  
Fa Zhao ◽  
Dong Liang ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Selection Algorithm ◽  
Wavelength Selection ◽  
Maize Seeds ◽  
Non Destructive

Quantitative detection of restructured steak adulteration based on hyperspectral technology combined with a wavelength selection algorithm cascade strategy

2021 ◽  
Vol 27 (6) ◽  
pp. 859-869
Author(s):  
Xiaoyu Liu ◽  
Zongbao Sun ◽  
Min Zuo ◽  
Xiaobo Zou ◽  
Tianzhen Wang ◽  
...  
Keyword(s):  
Quantitative Detection ◽  
Selection Algorithm ◽  
Wavelength Selection

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

Evaluation Method for Band Selection Algorithms of Hyperspectral Image

2013 ◽  
Vol 684 ◽  
pp. 495-498
Author(s):  
Bai He Wang ◽  
Shi Qi Huang ◽  
Yi Hong Li
Keyword(s):  
Data Fusion ◽  
Dimension Reduction ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Amount Of Information ◽  
Algorithm Evaluation ◽  
Common Band ◽  
Selection Algorithms

Band selection algorithm is most important in data dimension reduction of hyperspectral image. There are many algorithms of band selection, but there are only few methods to do algorithm evaluation. A method is put forward in this paper to evaluate the band selection algorithm of hyperspectral image. The amount of information, brightness, image contrast and definition are defined as 4 indexes to measure deferent data fusion based on various band selection results. Based on the measurement, the evaluation of band selection algorithm is realized. In the paper, the evaluation method is used in the compare of 4 common band selection algorithms, the result of measurement is analyzed and the feasibility is verified.

Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection

2018 ◽  
Vol 34 (5) ◽  
pp. 789-798 ◽  
Author(s):  
Yuechun Zhang ◽  
Jun Sun ◽  
Junyan Li ◽  
Xiaohong Wu ◽  
Chunmei Dai
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Population Analysis ◽  
Cadmium Stress ◽  
Hyperspectral Images ◽  
Cadmium Content ◽  
Variable Combination ◽  
Non Destructive

Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.

scholarly journals Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 97 ◽  
Author(s):  
Siddharth Chaudhary ◽  
Sarawut Ninsawat ◽  
Tai Nakamura
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Classification Model ◽  
Support Vector ◽  
Trace Detection ◽  
Svm Model ◽  
Detection Of Explosives ◽  
Non Destructive ◽  
Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

New Near Infrared Wavelength Selection Algorithm Based on Monte-Carlo Method

2010 ◽  
Vol 30 (12) ◽  
pp. 3637-3642 ◽  
Author(s):  
洪明坚 Hong Mingjian ◽  
温泉 Wen Quan ◽  
温志渝 Wen Zhiyu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Near Infrared ◽  
Selection Algorithm ◽  
Wavelength Selection ◽  
Infrared Wavelength

scholarly journals Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4391 ◽  
Author(s):  
Aimin Miao ◽  
Jiajun Zhuang ◽  
Yu Tang ◽  
Yong He ◽  
Xuan Chu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Procrustes Analysis ◽  
Waxy Maize ◽  
Maize Seeds ◽  
Variety Classification ◽  
Low Dimensional

Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

Sign in / Sign up

Export Citation Format

Share Document