Research on blue and white porcelain from different ages based on hyperspectral technology

Chronological classification studies of successive ages of Jingdezhen blue and white porcelain have high research value both academically and socioeconomically. Compared with other chemical analysis meth-ods, hyperspectral remote sensing techniques have advantages such as non-contact and non-destructive nature. In this paper, Jingdezhen blue and white porcelain of various ages is taken as the research ob-ject, linear discriminant analysis is used to build a model of typical extracted spectral features that are sensitive to blue and white porcelain material type and age information, stepwise discriminant analy-sis and competitive adaptive reweighted sampling are used for feature selection, and continuous wavelet transform and spectral feature parametric-based methods are used for feature extraction. Then, a random forest algorithm and long short-term memory (LSTM) are combined to categorize Jingdezhen blue and white porcelain of various ages. The stepwise discriminant analysis paired with LSTM is the most accu-rate combination amongst all classification techniques with the same amount of control preferred char-acteristics. This research shows that the categorization of Jingdezhen porcelain from various ages may be accomplished using hyperspectral remote sensing technique in conjunction with the random forest algorithm and long short-term memory.& COPY; 2023 Consiglio Nazionale delle Ricerche (CNR). Published by Elsevier Masson SAS. All rights reserved.

Automated summary

The chronological classification studies of Jingdezhen blue and white porcelain have high research value due to its academic and socio-economic significance. The paper uses hyperspectral remote sensing techniques to study Jingdezhen porcelain of different ages, and constructs a model of typical spectral features that are sensitive to the material type and age information of blue and white porcelain. The classification of Jingdezhen porcelain from various ages can be achieved using a combination of hyperspectral remote sensing technique, random forest algorithm, and long short-term memory.