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

Abstract

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 methods, 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 object, 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 analysis 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 accurate combination amongst all classification techniques with the same amount of control preferred characteristics. 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.