Comparison assessment of low rank sparse-PCA based-clustering/classification for automatic mineral identification in long wave infrared hyperspectral imagery

Yousefi, Bardia; Sojasi, Saeed; Ibarra-Castanedo, Clemente; Maldague, Xavier; Beaudoin, Georges; Chamberland, Martin

doi:10.1016/j.infrared.2018.06.026

Cited by 32 publications

(20 citation statements)

References 34 publications

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“…This result implies that violet and red regions (yellow, red, orange) have the most significance for identifying diseased rice seed. In other studies that have used hyperspectral image analysis, colormaps are usually presented with PCA, spectrum angle mapper and normalized cross correlation since they lead to identification of the target [35,36]. Williams et al (2009) [37] and Juan et al (2010) [38] depicted maize kernel hardness and sprout damage in Canada western red spring wheat via PCA score.…”

Section: Image Based Classificationmentioning

confidence: 99%

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

et al. 2019

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The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

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Section: Image Based Classificationmentioning

confidence: 99%

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

et al. 2019

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“…Dimension reduction methods, including PCA [31], independent component analysis (ICA) [32], and minimum noise fraction (MNF) [33], etc., were commonly used in hyperspectral imaging analysis to achieve a low-dimension database. However, PCA is one of the most popular methods in food quality detection due to its effectiveness and simplicity.…”

Section: Principal Component Analysismentioning

confidence: 99%

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019

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The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

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“…In addition, PCT is also suitable to be combined with other image processing techniques. [44][45][46]…”

Section: Principal Component Thermographymentioning

confidence: 99%

Nondestructive evaluation of low-velocity impact-induced damage in basalt-carbon hybrid composite laminates using eddy current-pulsed thermography

et al. 2018

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Comparison assessment of low rank sparse-PCA based-clustering/classification for automatic mineral identification in long wave infrared hyperspectral imagery

Cited by 32 publications

References 34 publications

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

Nondestructive evaluation of low-velocity impact-induced damage in basalt-carbon hybrid composite laminates using eddy current-pulsed thermography

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