ABSTRACT One of the particularities of spectral imaging when compared to single point spectroscopy is the importance of spectral pretreatments to minimize environmental and sample‐related effects, such as shadows, shapes, or variations in illumination. However, the influence of spectral pretreatments on distance metrics is rarely considered in any great depth. This work explores and discusses the effects of combining different spectral pretreatments with the most commonly used distance metrics. A case study on the classification of recyclable materials is used as an example. MATLAB code scripts and functions are provided and referenced through the article to allow the reader to follow and implement the process step by step. The theoretical basis for the calculation and choice of different pretreatments and distance metrics is explained in depth, and the classification performance of the different combinations of pretreatments and distance metrics is discussed in the light of these. Results show that distance metrics based on angle or correlation (i.e., Spectral Angle Mapper or Spectral Correlation Mapper) could be used with or without pretreatments without significantly impacting the classification results. Classification based on Euclidean and Cityblock distances was the most computationally efficient but was also the most affected by multiplicative effects in the spectra and thus benefited the most from pretreatments such as standard normal variate (SNV) or from combining SNV and second derivative. Lastly, Mahalanobis distance showed the best classification performance for nonpretreated spectra but showed the worst performance for SNV pretreated spectra, illustrating the importance of assessing spectral similarity between calibration and validation datasets on pretreated spectra when using Mahalanobis distance, particularly when applying spectral pretreatments. This work provides practical insights into the effects that the parameters used have on the results of distance‐based classification in terms of the performance of classification models. Considering this issue can greatly improve classification performance when assessing the potential of hyperspectral imaging systems for a particular application.
Herrero‐Langreo et al. (Thu,) studied this question.