# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 3 Future Perspectives
### Image Processing Algorithms
The approach provided new geological detail and allowed to identify areas of interest for geological and VHMS alteration mapping. According to the authors [118], the combination of supervised and unsupervised MLAs delivered a robust mean to analyze and map complex, challenging terrains. In 2016, [PERSON] et al. [119] compared the performance between RF and SVM in lithological mapping in India using Landsat-8, ASTER, and DEM data. Additionally, band-depth images from the analysis of laboratory spectra were used as input for the fuzzy inference system (FIS) to further discriminate between amphibite and mica-schist. Overall, the performance of RF and SVM was similar although RF provided a higher accuracy for the minor class [119]. [PERSON] and [PERSON] [120] compared SVM and ANN in hyperspectral mapping of ophiolite terrains in Iran using Hyperion images, the ANN having achieved higher accuracy. [PERSON] et al. [121], in 2018, assessed the performance of convolutional neural networks (CNN) in lithological mapping in Northwestern Canada. Different data layers were used to train the network including Landsat TM/ETM+ scenes, aerial photos, and high-resolution DEM data. The performance of CNN was evaluated in two sampling scenarios: the first uses samples collected over the area to be mapped, while the second scenario corresponds to an independent test area. Secondly, the performance of CNN was confronted with the one attained using the more widely used RF algorithm [121]. In the first scenario, both algorithms showed similar results with an average accuracy of around 76%. However, in the independent test sets, CNN outperformed RF by 4%. Most of these works show that the classification accuracy is improved when additional data is added to the satellite-acquired one. This follows the line of thought of [PERSON] [3] that remote sensing data by itself may not be sufficient to identify new mineral deposits. Therefore, it would be important to try to integrate satellite data with other types of data in Li exploration. This type of logic could also be applied to techniques employed, that is, several classifiers could be combined to form a better predictor (using ensemble methods like AdaBoost or bagging). Such an approach can help to overcome the weaknesses of single classifiers [122]. In the past recent years, several reviews have been published on either the use of a specific MLA or on the comparison of a set of machine and/or deep learning algorithms. [PERSON] et al. [72] presented various applications of SVM in remote sensing as well as several algorithmic advances and SVM variants developed. [PERSON] and [PERSON] [70] reviewed the use of RF as a classifier in remote sensing. The authors also highlight developments made and new applications in the field of remote sensing, besides pointing out future directions. [PERSON] et al. [123] reviewed MLAs from an applied perspective, focusing on SVM, decision trees (DTs), boosted DTs, RF, ANNs, and k-NN, the choice of proper algorithms, their implementation, training data requirements (class balancing problems for example), parameter tuning, computational costs, and availability of MLAs in remote sensing software packages. [PERSON] et al. [124] survey and compare the performances achieved by typical deep learning (DL) methods, such as model CNNs, stacked auto-encoders (SAEs), and deep belief networks (DBNs). The authors also presented a systematic review of developments made in pixel-wise and scene wise approaches using DL methods. Despite the great performance achieved by MLAs in the field of computer vision and image processing [122; 124], some difficulties are expected in what concerns their application to Li exploration. The first and most obvious obstacle is the in-depth knowledge about the MLAs and DL required for any geoscientist/geologist to conduct such an approach. The second is related to the nature of the classification problem since mineral exploration in general, and Li exploration in particular, inherently differ from classical applications, such as LULC problems.