# AetherGeo: A Spectral Analysis Interface for Geologic Mapping
## 1 Introduction  
Using data that describe variations in how each given material interacts with light across different wavelengths is a common technique for characterizing materials at any scale [1, 2]. Thus, optical remote sensing centers around acquiring variations in how the surface material interacts with the light from the sun. Machine learning (ML) algorithms are typically applied to process such remote sensing data due to the high quantity of data, and this is a topic that has experienced exponential growth in the last few years [3]. Multiple sensors onboard different satellites are available for data acquisition by academic and non-academic communities, either multispectral or hyperspectral [3], and remote sensing techniques have shown great potential in identifying variations in materials present in the ground, therefore having extensive potential for geological applications [4, 5]. One of the main current problems is the limited access potential users have to tools that enable efficient processing and analysis of such data in free, all-in-one software. For this reason, building a tool focused on geological applications and data analysis seems to meet the demand for specific applications that are free and open-source [6]. In recent years, a limited number of software programs have been developed to provide possible users access to such tools, especially outside the scope of proprietary packages such as \"Environment for Visualizing Images\" (ENVI) [7]. Some alternatives have recently been proposed [8; 9; 10; 11]. We believe that such projects are a step toward giving research and educational communities access to free open-access software. HypeRvieW [8] is a license-free software that runs on the Linux operating system. While it is developed using the C language, it provides great capabilities for spatial-spectral supervised classification. Another important feature is the integrated 2D noise correction function, which uses wavelets and soft-thresholding [12]. Its main limitation is the lack of compatibility with more common file types for remote sensing optical images from satellite missions. HypPy [9] provides a different range of tools, focusing on taking advantage of the physical meaning of the wavelength values in place of band numbers. Another advantage is compatibility with all default ENVI file formats. While not focused on typical endmember extraction, it can perform mapping based on variations in central wavelength position and relative depth, making it a valuable tool for spectral analysis. HYPER-Tools [10] is a MATLAB-based [13] interface that provides extensive tools for the preprocessing and analysis of spectral data. For the second component, the functions range from dimensionality reduction, endmember identification, clustering, and classification. The main limitation is the dependency on proprietary software. EnMap-box [11] is a plugin for QGIS that provides an extensive set of tools for hyperspectral and multispectral image analysis in a Geographic Information System (GIS) environment. One of the main advantages is the time series visualization function. Though developed to focus on spectral analysis in a broader sense, it also provides some specific tools for geological applications across multiple fields of study. One of the main contributions of AetherGeo is providing a 3D interactive point cloud tool for endmember selection. Considering some of the limitations identified in other free alternatives, we propose a new software, AetherGeo, with similar capabilities to the mentioned tools but with significant new functions: (i) a higher number of tools/algorithms for dimensionality reduction, (ii) density-based clustering algorithms for classification, and (iii) 3D point cloud selection for endmember extraction. Additionally, AetherGeo is not limited by proprietary components in any of its functionalities, making it a pertinent contribution to the current state-of-the-art technology. A more detailed comparison with other alternatives can be found in Appendix A.