## 1 Introduction  
During the last decades, remote sensing applications on geological mapping and exploration of mineral deposits have grown rapidly [1; 2; 3; 4; 5; 6]. In particular, the utilization of multispectral and hyperspectral satellite imagery for the recognition of spatial and/or spectral patterns related to alteration is one of the most widespread and standard procedures in field geological exploration because of their high efficiency and low cost. To this purpose, the development of new state-of-the-art processing algorithms has become crucial for the lithological and mineralogical mappings linked with hydrothermal active environments [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. The importance of using satellite imagery in mineral exploration, in order to detect mineralization-related alterations, is significant not only because hydrothermal zones are associated with the presence of a high number of minerals [26], but also because they are associated with ore deposits and geothermal systems [27]. Volcanic activity is related directly with hydrothermal alteration processes [28]. For this reason, there are numerous studies using image processing techniques on satellite data [18; 19; 20; 21; 29; 30] for ore deposits' exploration in volcanic environments with economic interest [31; 32; 33]. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+), Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are the most widely used multispectral satellite sensors for the detection and mapping of minerals related to alteration, such as iron oxides, hydroxyl-bearing minerals, and carbonates [20; 24; 34; 35; 36; 37; 38]. Especially, ASTER provides spectral bands in the shortwave infrared (SWIR) part of the electromagnetic spectrum which, in comparison with the Landsat sensors, are highly sensitive to alteration minerals and, thus, offers a powerful tool in mineral exploration and accurate mapping of hydroxyl-bearing groups of minerals such as clay minerals [19; 39; 40; 41]. Furthermore, since 2015, several studies have been conducted to reveal the potential of Sentinel-2 Multispectral Imager (MSI) in geological remote sensing [1; 16; 42; 43]. Several processing methodologies) have been proposed during the last decades. They can be distinguished in two main categories: those that perform processing at pixel level and those that perform processing at subpixel level. The former refers mainly to (both supervised and unsupervised) classification methods, where each pixel is assigned to a single class. Such well-known methods include Spectral Feature Fitting (SFF), Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) [44; 45; 46; 47], Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Extreme Learning Machines (ELM) [48; 49; 50], and clustering techniques [51; 52; 53]. On the other hand, the subpixel-level processing refers to the case where multiple pieces of information are available for each pixel. This category includes (the more traditional) spectral indices (SIs) and spectral unmixing (SU) methods. Since the early 1980s, the calculation of SIs (scalar quantities, which are usually defined by various band combinations) [54; 55; 56; 34] has allowed the enhancement and quantification of spectral differences within a multispectral image due to different lithologies [57]. Different SIs can reveal different aspects of information regarding the materials' composition of the image pixels. Especially, in hydrothermal alteration mineral mapping, the use of SIs has provided efficient results in the differentiation between hydrothermally altered and non-altered lithologies [58; 59; 60; 61; 62; 63]. Concerning SU, the aim is the decomposition of the spectral signatures of mixed pixels into a selection of spectral signatures (represented as vectors) corresponding to the reflectance of pure physical materials (endmembers) [64].