Considering the non-contact limitation when analyzing the different surfaces, multispectral and hyperspectral images has become more relevant in different sectors. Among others, they are used in the automotive sector allowing the analysis of goniochromatic pigments used in automobile coatings [1], in the agriculture sector [2] or in the preservation and restoration of cultural heritage [3].

The main disadvantage of commercial hyperspectral systems are their high price. Research is focused on developing low-cost systems without sacrificing the high precision of commercial hyperspectral imaging systems [4,5].

Materials and methods

A hiperespectral image capture system consisting of a matrix of spectral filters has been optimized [6]. This system allows to obtain hyperspectral images in a shorter time compared to the traditional filter wheel method since each spectral filter has a sensor. The control of the system is carried out through Python software and provides images with 4K resolution and 12-bit color depth, obtaining images with 4096 maximum gray levels.


A multispectral image capture system has been designed, which can be managed through software that allows remote control.

Using the developed multispectral image capture system, it has been possible to obtain multispectral images with 4K resolution and 12-bit color depth