Unsupervised Classification of Hyperspectral Images using PCA and K-Means

Malik, Aamir Saeed; M, Waheed

doi:10.20944/preprints202106.0634.v1

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…The K-means is a widely used and validated method for market segmentation, which utilises a machine learning algorithm to associate similar data points and understand the underlying patterns presented (52). Further, a gap statistic was applied in R studio (53) to verify the resulting three clusters solution, "All Low, " "High meat, low seafood, " and "All High. "…”

Section: Discussionmentioning

confidence: 99%

Consumers’ willingness to buy meat and seafood products close to the expiry date: an exploratory study from Denmark

Pandey,

Bohl,

Favari

et al. 2024

Front. Nutr.

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IntroductionMeat- and seafood products close to their expiry date (MSPCED) are one of the significant contributors to the growing food waste. Therefore, this study aims to investigate consumers’ attitudes and willingness to buy MSPCED.MethodsAn online questionnaire was used to collect data from 400 Danish consumers.Results and discussionThree consumer segments were identified based on their willingness to buy MSPCED: 39.8% of the participants showed a high willingness to buy MSPCED close to their expiry date (“All High”), 34.5% were selective with a high willingness to buy meat close to their expiry date but not seafood (“High meat, low seafood”), while 25.7% showed a low willingness to buy MSPCED (“All Low”). Consumers’ willingness to buy MSPCED is influenced by the perceived quality of the products, food safety, social acceptability, and price. Consumers were willing to pay a higher price for minced beef close to the expiry date when compared to cod fillets, pork cuts, and chicken breasts. However, consumers were willing to buy cod fillets with the lowest discount percentage when compared to pork cuts, minced beef, and chicken breast. The findings suggest that price reduction and discount percentage can have varying effects in influencing willingness to pay for MSPCED. This study provides valuable insights, for food waste practitioners in the retail sector, to develop effective strategies for reducing food waste by influencing consumer willingness to buy and pay for perishable products like meat and seafood.

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Section: Discussionmentioning

confidence: 99%

Consumers’ willingness to buy meat and seafood products close to the expiry date: an exploratory study from Denmark

Pandey,

Bohl,

Favari

et al. 2024

Front. Nutr.

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show abstract

“…The eigenvalues of the covariance matrix signify the variance of the eigenvector. 14 The outcome is a number of images dependent on the chosen components at the beginning of the computation. As the number of components increases, the amount of variance decreases.…”

Section: Identification With Pcamentioning

confidence: 99%

Advancing recycling efficiency through hyperspectral identification of fabric blends

Guay,

Ferland,

Lachapelle

2024

Photonic Instrumentation Engineering XI

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Textile sorting for recycling and revalorisation is a multifaceted challenge that requires accurate material classification. We demonstrate the effectiveness of short-wave infrared (SWIR) hyperspectral imaging as a method employed to address this challenge. The utilization of various data processing strategies enables us to ascertain the accuracy of textile blends. Employing the Multivariate Curve Resolution-Alternating Least Square algorithm, we establish an uncertainty range of ±2.7 -5.0% using pure elements as a training set. To achieve this, we employ multiple pre-processing methods to enhance the spectrum and assess alternative regression algorithms, such as Multivariate Regression-Partial Least Square and Principal Component Algorithm. Additionally, we conducted tests using two hyperspectral systems with distinct spectral ranges: one extending up to 2500 nm and the other up to 1700 nm. Furthermore, a study on the influence of fabric color on regression and textile spectra was conducted.

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“…K-means clustering was then performed on the four principal components to yield 10 clusters (k = 10). K-means is an unsupervised learning algorithm that partitions a pre-defined number of clusters in such a way that within-cluster variance is minimised to the greatest extent possible (Lloyd, 1982;Malik and Tuckfield, 2019). The elbow method (Thorndike, 1953) was initially attempted to determine what the optimal value of k was, but the results were inconclusive.…”

Section: Seafloor Morphology Classificationmentioning

confidence: 99%

Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

Sklar,

Bushuev,

Misiuk

et al. 2024

Front. Mar. Sci.

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Detailed maps of seafloor substrata and morphology can act as valuable proxies for predicting and understanding the distributions of benthic communities and are important for guiding conservation initiatives. High resolution acoustic remote sensing data can facilitate the production of detailed seafloor maps, but are cost-prohibitive to collect and not widely available. In the absence of targeted high resolution data, global bathymetric data of a lower resolution, combined with legacy seafloor sampling data, can provide an alternative for generating maps of seafloor substrate and morphology. Here we apply regression random forest to legacy data in the Gulf of St Lawrence, Canada, to generate a map of seabed sediment distribution. We further apply k-means clustering to a principal component analysis output to identify seafloor morphology classes from the GEBCO bathymetric grid. The morphology classification identified most morphological features but could not discriminate valleys and canyons. The random forest results were in line with previous sediment mapping work done in the area, but a large proportion of zero values skewed the explained variance. In both models, improvements may be possible with the introduction of more predictor variables. These models prove useful for generating regional seafloor maps that may be used for future management and conservation applications.

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Unsupervised Classification of Hyperspectral Images using PCA and K-Means

Cited by 5 publications

References 12 publications

Consumers’ willingness to buy meat and seafood products close to the expiry date: an exploratory study from Denmark

Consumers’ willingness to buy meat and seafood products close to the expiry date: an exploratory study from Denmark

Advancing recycling efficiency through hyperspectral identification of fabric blends

Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

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