# AetherGeo: A Spectral Analysis Interface for Geologic Mapping
## 2 Spectral Data Analysis  
\"Light\" is commonly used to describe the visible component of the electromagnetic spectrum, but in reality, optical data acquired with sensors can span more than the visible component, something important for the characterization of materials and commonly used for the differentiation of minerals in geological applications (Figure 1) [23]. This ability to characterize materials is associated with light's interactions with materials, which vary based on their chemical and physical properties. For this reason, the use of optical data for characterization via remote sensing is a topic that has been used for a long time in multiple domains of study, especially those related to remote sensing [24; 25; 2]. The launch of the Hyperion-01 sensor [26] marked a significant advancement in the application of hyperspectral data for different fields of study, with a special emphasis on environmental and geological applications [5; 27]. Recent advancements in sensor technology, noise levels, and the launch of new satellites due to \"Precursore Iper5 Peptidase della Missione Applicativa\" (PRISMA) and \"Environmental Mapping and Analysis Program\" (EnMap) missions not only provide easier access to quality data but also incentivize the need to take full advantage of these data, which have a lot of potential but require more detailed processing due to their high complexity. It is also important to introduce the difference between multispectral and hyperspectral data (Figure 2). The first one is better described as each pixel having a discrete set of associated variables (based on the number of bands), while in the other case, the bands are narrow (2-10 nanometers), and therefore, there is a large number of bands covering the zone of the electromagnetic spectrum being measured [29; 30]. This continuous property of the spectra associated with hyperspectral data makes them a powerful tool for earth surface characterization [31], and therefore, they have significant potential for geological applications. The capacity for academic and non-academic communities to have access to such data covering extensive areas has been greatly improved by the recent PRISMA and EnMap, with the added benefit of these products being commonly provided with level 2A and, therefore, having already been corrected for atmospheric effects, making integration with the rest of the analysis workflows faster and easier. Additionally, access to hyperspectral satellite data is expected to increase in the future due to new missions such as the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) by the European Space Agency (ESA), which consists of two satellites (CHIME-A and CHIME-B) providing systematic hyperspectral data [32]. Figure 1: (**a**) Almandine mineral. (**b**) Respective infrared spectrum [28]. Though sensors onboard unmanned aerial vehicles (UAVs), aircraft, and spacecraft can acquire such images, access to proper techniques for processing and inspecting such data is essential for retrieving significant information. Traditional techniques, such as false color RGB combinations, allow for fast, somewhat intuitive evaluation of the overall scenery but lack concrete interpretability of results and capabilities to consider more than three bands. It is also common to use unsupervised learning algorithms when analyzing spectral data since they provide data-driven results that uncover complex links in data [34] without requiring labeled data. The most commonly used unsupervised approaches for analyzing remote sensing optical data are clustering and dimensionality reduction algorithms, the latter being especially important for hyperspectral data due to what is described in the literature as the curse of dimensionality [35]. Another alternative is spectral unmixing, especially algorithms under the category of partial spectral unmixing, since they do not rely on having the full information for each component present in the image [36], something unreasonable for most geological applications.