Low-Cost Filter Selection from Spectrometer Data for Multispectral Imaging Applications

Imaging beyond RGB bands has the ability to provide solutions for non-destructive detection of objects or scene properties. Methods often used in research include hyperspectral cameras and spectrometers; the former is expensive and the latter can only provide point measurements. Multispectral imaging can provide low-cost solutions in instances where RGB does not suffice, however, evaluation of filters used in multispectral sensors or the use of off-The-shelf filters for user specific applications have not been explicitly analysed. This paper proposes a novel method for using spectrometer data by firstly limiting the search space to existing off-The-shelf filters, modelling those filters, and then applying the desired model for classification or regression to more appropriately model multispectral imaging performance. The results indicate that point measurements produced by spectrometers may not correspond with the response achieved when using off-The-shelf filters; particularly in the instance where the spectral profile of the measured object varies significantly within the transmission regions of the filter. The method presented focuses on the practical estimation of filter performance when selecting filters.