# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 3 Future Perspectives
### Image Processing Algorithms  
In the past, several authors have reviewed the different techniques and image processing algorithms that can be applied to geological remote sensing in general [102; 103] or in mineral exploration [5; 6]. Some even group the image processing methods into functional categorization schemes. [PERSON] [104] defined three categories: (i) image restoration; (ii) image enhancement; and (iii) information extraction. Later, in 2016, [PERSON] and [PERSON] [103] proposed a new scheme in which the techniques are divided into knowledge-based and data-driven approaches. Common image processing algorithms employed in mineral exploration are: (i) RGB combinations [105; 106]; (ii) band ratios [6; 105]; (iii) PCA [28; 107]; (iv) MNF [21; 105; 106]; (v) matched filtering (MF) [24; 105]; (vi) linear spectral unmixing (LSU) [11; 51]; (vii) constrained energy minimization  
Figure 4: Comparison between ASTER, Landsat 8, Sentinel-2, and WorldView-3 bands. Adapted from USGS Landsat Program [100]. ASTER [81]: 1 green; 2 red; 3N/3B—NIR nadir-looking/NIR backward-looking; 4–9 SWIR; 10–14 TIR. Landsat-8 [89]: 1 coastal aerosol; 2 blue; 3 green; 4 red; 5 NIR; 6 SWIR 1; 7 SWIR 2; 8 Panchromatic; 9 cirrus; 10 TIRS 1; 11 TIRS 2. Sentinel-2 [88]: 1 coastal aerosol; 2 blue; 3 green; 4 red; 5, 6, 7, and 9 vegetation red edge; 8 NIR; 10 cirrus; 11 and 12 SWIR. WorldView-3 [99]: 1 coastal; 2 blue; 3 green; 4 yellow; 5 red; 6 red edge; 7 NIR 1; 8 NIR 2; 9–16 SWIR. (CEM) [17; 21]; (viii) least square fitting method [105]; (ix) independent component analysis [50]. and supervised algorithms such as (x) SAM [8; 108], and (xi) MTNF [8; 15]. Methods such as MNF, PPI, and SAM have already been applied to pegmatite detection in Southern Namibia using airborne hyperspectral data (HyMap) [109]. For the same region, [PERSON] et al. [110] proposed a new classification method named continuum-removal MSAM that combined the Modified Spectral Angle Mapper (MSAM) and continuum-removal methods. Using HyMap images, the authors produced mineral index maps to target common pegmatite minerals such as lipidolite and other white micas. In the Cornwall region, United Kingdom, [PERSON] and [PERSON] [111] used lepidolite as a pathfinder for topaz-bearing granites in an environmental monitoring study. For that, HyMap airborne images were used and methods like MNF, PPI, and MTMF were employed. Recently, great attention has been given to non-parametric classifiers such as machine and deep learning algorithms. These techniques have been applied in land-use/land-cover (LULC) problems with great success, outperforming classical algorithms [112; 113; 114; 115]. In 2014, [PERSON] et al. [116] conducted a meta-analysis and synthesis of satellite-based land-cover mapping studies, concluding that, from all classification methods (used more than 10 times out of the 1651 analyzed studies), MLAs, namely ensemble classifiers, ANN and SVM, are the methods that achieved a performance better than the average accounted in the 1651 experiments. Although the number of applications is smaller when compared to the LULC studies, similar results have been obtained in lithological mapping and mineral exploration [40; 117; 118; 119; 120; 121]. [PERSON] et al. [117], in 2012, developed an automated approach to lithological mapping in Northwestern India where different SVMs with the radial basis function (RBF) kernel were trained using ASTER, ASTER-derived digital elevation model (DEM), and aeromagnetic data. The tested number of input features ranged from 14 to 33, having the classification accuracy increased with the increasing number of layers. When compared with a classical classifier, SVM clearly outperformed ML, achieving a much higher accuracy [117]. [PERSON] et al. [118] applied RF and self-organizing maps (SOM) to map lithology and volcanic-hosted massive sulfide (VHMS) alteration in Tasmania. The input data included airborne geophysical, soil geochemical and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) spectral data.