  Multispectral and hyperspectral images are increasingly popular in different
research fields, such as remote sensing, astronomical imaging, or precision
agriculture. However, the amount of free data available to perform machine
learning tasks is relatively small. Moreover, artificial intelligence models
developed in the area of spectral imaging require input images with a fixed
spectral signature, expecting the data to have the same number of spectral
bands or the same spectral resolution. This requirement significantly reduces
the number of usable sources that can be used for a given model. The scope of
this study is to introduce a methodology for spectral image data fusion, in
order to allow machine learning models to be trained and/or used on data from a
larger number of sources, thus providing better generalization. For this
purpose, we propose different interpolation techniques, in order to make
multisource spectral data compatible with each other. The interpolation
outcomes are evaluated through various approaches. This includes direct
assessments using surface plots and metrics such as a Custom Mean Squared Error
(CMSE) and the Normalized Difference Vegetation Index (NDVI). Additionally,
indirect evaluation is done by estimating their impact on machine learning
model training, particularly for semantic segmentation.
