Hyperspectral Image-Based Variety Discrimination of Maize Seeds by Using a Multi-Model Strategy Coupled with Unsupervised Joint Skewness-Based Wavelength Selection Algorithm

2016 ◽  
Vol 10 (2) ◽  
pp. 424-433 ◽  
Author(s):  
Sai Yang ◽  
Qi -Bing Zhu ◽  
Min Huang ◽  
Jian-Wei Qin
Keyword(s):  
Hyperspectral Image ◽  
Selection Algorithm ◽  
Wavelength Selection ◽  
Maize Seeds

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

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.

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.

scholarly journals Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectance Imaging

Molecules ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 149 ◽  
Author(s):  
Jun Zhang ◽  
Limin Dai ◽  
Fang Cheng
Keyword(s):  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Principal Component ◽  
Projection Algorithm ◽  
Support Vector ◽  
Wavelength Selection ◽  
K Nearest Neighbor ◽  
Corn Seed ◽  
Freeze Damage

A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

Peak-hopping stepwise wavelength selection algorithm

1999 ◽  
Author(s):  
Michael J. McShane ◽  
Brent D. Cameron ◽  
Gerard L. Cote ◽  
Clifford H. Spiegelman
Keyword(s):  
Selection Algorithm ◽  
Wavelength Selection

Band selection algorithm based on information entropy for hyperspectral image classification

2017 ◽  
Vol 11 (2) ◽  
pp. 026018 ◽  
Author(s):  
Li Xie ◽  
Guangyao Li ◽  
Lei Peng ◽  
Qiaochuan Chen ◽  
Yunlan Tan ◽  
...  
Keyword(s):  
Image Classification ◽  
Information Entropy ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Hyperspectral Image Classification
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