## 2 Materials and Methodology
### Hyperspectral Band Selection  
The redundancy of information among bands in HS images is a well-known problem, which can be attributed to the relatively high spectral resolution. HS images typically comprise a large number of bands, resulting in high-dimensional data. The high dimensionality leads to considerable computational demands and high storage costs. Therefore, to analyze the 41-dimensional HS data cubes captured by our system, we apply a dimensionality-reduction processing step. Dimensionality-reduction techniques can be divided into band extraction and band selection methods. One of the most common band extraction methods is principal component analysis (PCA), which projects the data in a low-dimensional space where the spectral information is decorrelated [29]. PCA  
Figure 4: **(a)** Experimental set-up view, where the hyperspectral camera and the light source are mounted on a tripod. **(b)** The back of the subject is scanned by using optical markers as references (see the magnified view box).  
generates a new set of linearly uncorrelated variables, where the first few retain most of the signal variation occurring in all original variables. However, the linear projection transformation leads to the mixing of the original spectral information.  
It should be noted that this mixing phenomenon poses an important issue for images with different scales or changes in spatial structures [30]. For this reason, we investigate an alternative that enables us to specifically address individual spectral bands. This alternative is found in band selection algorithms.  
Band selection algorithms select a subset of bands without losing their physical meaning and have the advantage of preserving the relevant original information. Since we are interested in detecting local features in the spectral images, we have adopted the concept of [PERSON] et al. [31]. In their work, the authors defined a Saliency-Band-Based Selection algorithm (SSBS) for HS images. A saliency band is a band that is capable of characterizing multiple objects and is rare in the HS data. In order to select salient bands with high information content, the method proposes the adoption of scale-selection techniques which can characterize the objects of an image [32]. Based on this concept, the authors proposed to imitate the optimal scale-selection process. In our previous work, Speed Up Robust Features (SURF) [33] achieved the best result in detecting MS skin features when captured at different scales. For HS images in which the target may mingle with the background, it is reasonable to use a scale-selection technique and find extremum points for selecting salient bands, as suggested in [31]. We calculate the determinant of the Hessian matrix for the entire HS data cube in order to determine the local extrema for each band. A salient band is selected if it has a higher amount of local extrema compared with its neighboring bands (i.e., left and right adjacent bands).  
For benchmarking, we also applied an unsupervised band selection algorithm, which was the fast Volume-Gradient-Based Band Selection (VGBS) method from [PERSON] et al. [34] because it offers a higher accuracy in comparison with other band selection methods [31]. VGBS is a geometry-based method that identifies the bands with the maximum ellipsoid volume for capturing spectral information. The bands are treated as data points in a high-dimensional space. At the beginning of the algorithm, all bands are considered as candidate bands and constitute a parallelotope. Then, bands leading to the minimal losses of the parallelotope volume are removed iteratively, until the desired number of bands in the subset remains. VGBS can effectively reduce the correlation among bands, since low-correlated bands often represent a large-volume parallelotope [34].