# Hyperspectral Python: HypPy
## 3 Application Areas
### Research  
The first work on HypPy started with the Mars Planetary Mapping Pilot Project for the European Space Agency (ESA). The pilot study was designed to transfer the modern, digital methods and workflows used in terrestrial geological mapping to planetary mapping. The focus of this study was placed on the ESA datasets from the High Resolution Stereo Camera (HRSC) and the Visible and Infrared Mineralogical Mapping Spectrometer (OMEGA) hyperspectral instrument onboard the Mars Express [31]. The first step of the workflow was to process the raw data to highlight features of interest in derived information products. These included digital elevation models extracted from the stereo data and mineral maps made using the hyperspectral data. These tools were used to interpret the data and produce a geological map for the Nili Fossae area. This study paved the way for the wavelength mapping technique to be adopted for mapping the planets Mars and Earth on a routine basis [7]. HypPy introduces a processing methodology designed to transform OMEGA data from radiance-at-sensor to surface reflectance. To address particular challenges associated with OMEGA data, we devised innovative approaches for determining a per-scene transmittance spectrum, conducting atmospheric correction, and filtering systematic and random noise. See, for instance, [PERSON] et al. [25]. These filters are now part of HypPy. The processing methodology generates surface reflectance images, allowing the extraction of single-pixel spectra for surface feature identification. In alignment with various prior studies, our findings suggest, among other observations, the existence of iron-rich clays on the Martian surface. Mapping physical properties from hyperspectral images is complicated and frequently depends on the accessibility of spectral libraries of the target materials. Many existing methods depend on the subjective decisions of the interpreter for the selection of endmembers and the postprocessing of rule images. If no prior information is available, information should be extracted by the characterization of spectral absorption features. In [PERSON] et al. [25], they describe a method called the wavelength mapper (see Section 2.4.4) to automatically determine the minimum reflectance within a wavelength range. We conclude that the wavelength mapper proves to be a useful tool for unbiased endmember selection, mineral mapping, and the interpretation of areas demonstrating zonation of mineral alterations using hyperspectral imagery. The usefulness of the wavelength mapper has already been recognized in other research. For instance, it was used to map mineral outcrops of lithium-bearing pegmaties in a pilot quarry located near Vis, Namibia [32]. In another study, the entropy function was added to the spectrum class to enable the calculation of an index for sorting processes in hydrothermal systems. Hydrothermal processes affect the composition of rocks by chemical reactions and fluid flow, and this paper showed that the remaining patterns of such processes can be visualized using the Shannon entropy. The application of entropy is useful for studying hydrothermal mineral deposits and may be valuable in studies of early life environments as well. Functions that have already been implemented in the spectrum class of HypPy could be used to add noise to data. This was used for the simulation of airborne SWIR hyperspectral sensors for the mapping of methane (CH\({}_{4}\)) gas [33]. The study's findings indicated that three presently active airborne imaging spectrometers were unsuccessful in detecting the CH\({}_{4}\) plume. The paper emphasized the importance of both spectral sampling and radiometric calibration for the successful mapping of methane. Contemporary imaging spectrometers are compact, lightweight, cost-effective, and applicable in laboratory settings. Precise hyperspectral images necessitate minimal noise, precise calibration, and consistent response uniformity. Periodic checks are advisable due to the aging of optical and electronic components.