The emergence of hyperspectral cameras (NIR-VIS) has made it possible to acquire millions of spectra on samples. This has generated a need to use data processing and visualization methods because manual observation is no longer possible. However, when the data becomes complex with variations in recipes, intensity or mixture within the same dye or pigment, common methods of segmentation no longer work very well (classification according to the intensity and not the shifts visible on the spectra for example). Pearson correlation-based data treatment is developed and discussed in this paper. We find the use of reflectance spectra to answer questions in many cases. For example, the study of 18th century Aubusson tapestries dyes by crossing hyperspectral imaging and other non-invasive analyses methods is carried out for dye identification purposes [1]. Another illustration is the use of hyperspectral imaging on Iznik ceramics tiles inside the Saint-Maurice Residence (Cairo). Patterns similar to those of the residence exist dotted around Cairo [2]. Their study allows for traceability in the context of reuse. But in both cases, problems arise due to huge amounts of data for variations of the same pigments and dyes [3], and therefore we needed to develop a new method to reduce them. This study proposes a method to enhance the robustness of hyperspectral images processing and reduce the amount of data by generating tools for a similarity study between studied spectra and a database. The first step is the creation of a database of key spectra used for correlations. Then, some pre-processing are applied to the studied hyperspectral imaging (like spatial filtering for denoising). The main point of our method is that we compute a Pearson correlation coefficient between the studied spectra and each of the key spectra from the database. These new values obtained can be used for common methods of segmentation and visualisation. Our processes have been applied to different cases. After testing it on simple dyes colour charts with an internal database for validation, we applied it to the tapestry with an external dyes colour chart to obtain a PCA in which the clusters are much sharper than a classic PCA on raw data. It therefore allowed a finer identification of the dyes. As an illustration, we identify the red dyes on a tapestry as madder over cochineal dye. Finally, the method was applied to groups of pigments on the Iznik ceramic tiles and made it possible to drastically reduce the amount of data by keeping only the relevant information. Visualizations help to show similarities or dissimilarities across tile patterns. These cases illustrate the improvement in robustness while reducing the amount of data in relevant criteria for the similarity of dyes or pigments.