Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang, Yuling; Ou, Xingqi; He, Hong-Ju; Kamruzzaman, Mohammed

doi:10.1016/j.fochx.2024.101235

Cited by 2 publications

(1 citation statement)

References 131 publications

(167 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current methods developed for overcoming the lighting issue in vision-based methods, and especially in hyperspectral imaging, have been advanced classification methods, such as classifiers operating on spectral–spatial data [ 18 , 19 , 20 ], where instead of classifying each pixel alone, the data about the neighbouring pixels and location of the pixel in question are also included in the learning data [ 21 ], which lowers the method’s sensitivity to imperfect lighting. This method, however, performs best in scenarios where different endmembers (classes) present on the hyperspectral image are of a different chemical composition from one another, resulting in a high separation of those classes.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images

Długosz,

Dao,

Staszewski

et al. 2024

Sensors

View full text Add to dashboard Cite

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used.

show abstract