# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Tools
#### 2.4.4 Wavelength Mapper  
Mapping physical properties from hyperspectral images is complicated and often relies on the availability of spectral libraries of target materials. Many existing methods depend on the subjective decisions of the interpreter for the selection of endmembers and the post-processing of rule images. If no prior information is available, information should be extracted by characterization of spectral absorption features. On multi-spectral images, such features can be found using indices based on band ratios and band depths; see, for instance, [23]. However, hyperspectral images show more spectral detail of the features, which opens up the possibility of using more sophisticated methods.  
Absorption features of spectral curves are important for analysis and interpretation. Such features may be parametrized as center wavelength position, depth, area, and asymmetry [24]. Usually, the most important features are the center wavelength position and the depth of the main absorption feature. The wavelength position of an absorption feature can be used for the identification of minerals because many minerals have their own unique absorption wavelengths. The depth of such a feature is an indication of the abundance of a mineral.  
The center wavelength and depth of the deepest feature and consecutive deepest features can automatically be determined. These parameters can be calculated for all the pixels (spectra) of a hyperspectral image and presented as grey-level images representing the wavelength and depth of the feature.  
Subsequently, the resulting wavelength and depth images can be fused using an HSI (hue-saturation-intensity) transform [17] in such a way that the resulting image shows the wavelength in color (the hue). The intensity represents the depth of the feature. The saturation is set to 1. The wavelength mapper tool of HypPy automatically determines the wavelength and depth of the deepest absorption feature of all the pixels of a hyperspectral cube and fuses the two into one color image.  
Figure 7a shows a rock sample in a natural color composite. Figure 7b shows the result of the wavelength mapper; Figure 7c shows the legend to the wavelength map. The REE-containing monazite is visible as orange grains. The greens and the blues are carbonates. For a further discussion of this rock sample, see [16].  
The wavelength mapper is unsupervised, straightforward, and repeatable; large areas can be mapped at once, and details are preserved within the focus of the chosen spectral range. The result, called the wavelength map, can be understood by users with a limited image processing background. However, although the colors may suggest the presence of certain minerals, the resulting wavelength map is not a mineral map. The wavelength map can be used as a pre-classification step in order to understand the spectral variability in the scene. The knowledge obtained from the wavelength map is useful for the additional steps of producing a mineral map.  
The wavelength mapper relies on only two input parameters: the wavelength range to focus on, and the stretch to use for the depth of the feature. The wavelength mapper was first published in [PERSON] et al. [25], and a tutorial on the method was published in [24].