# Multi-Source Satellite Imagery and Machine Learning
## 7 Discussion  
The results of this study highlight the crucial importance of geographic and spectrometric parameters in enhancing the performance of geological classification models. The model performances vary significantly depending on the datasets and algorithms used, revealing interesting trends and potential areas for improvement. The superior performance of the Random Forest model in most data combinations can be attributed to its ability to handle heterogeneous and noisy datasets while limiting overfitting through its tree aggregation mechanism. For instance, the combination of geographic parameters and principal component analysis data (DGP-DPCAP) achieved an accuracy of 55.29% and an MCC of 45.12%, surpassing other tested combinations. A particularly notable result is the high accuracy achieved in the classification of alluvium, reaching up to 93% with the Random Forest algorithm. This effectiveness can be explained by several geological factors. Alluvium typically exhibits distinct spectral and morphological characteristics due to its sandy, silty, or clayey composition, which differs markedly from denser and more coherent rock formations. Additionally, its topographic location in valleys and plains, often accompanied by a dense hydrographic network, facilitates detection using geographic parameters, such as low slope and proximity to watercourses. These features make alluvium easier to identify using classification algorithms compared to more complex lithologies or those obscured by dense vegetation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & **Algorithm** & **Time (ms)** & **Accuracy (\%)** & **MCC (\%)** \\ \hline DGP-DOLIP & Gradient Boosting & 14.08 & 50.51 & 39.14 \\ DGP-DOLIP & XGBoost & 7.91 & 51.53 & 40.35 \\ DGP-DOLIP & Random Forest & 3.16 & 51.53 & 40.28 \\ DGP-DPCAP & Gradient Boosting & 17.88 & 50.85 & 39.59 \\ DGP-DPCAP & XGBoost & 5.95 & 50.85 & 39.66 \\ DGP-DPCAP & Random Forest & 3.59 & 55.29 & 45.12 \\ DPCAP-DOLIP & Gradient Boosting & 15.99 & 41.29 & 27.12 \\ DPCAP-DOLIP & XGBoost & 5.61 & 40.27 & 26.34 \\ DPCAP-DOLIP & Random Forest & 3.38 & 45.05 & 31.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Algorithm performance evaluation on different datasets (DGP-DOLIP, DGP-DPCAP, DPCAP-DOLIP). However, this high performance for alluvium raises fundamental questions about the complexity of the methods used. The detection of alluvium could potentially be achieved using simpler models, such as a high-resolution Digital Terrain Model (DTM). Geomorphological analysis techniques in a Geographic Information System (GIS), could delineate valley bottoms and alluvial extents with comparable or even superior accuracy. It is therefore essential to refocus the use of such algorithms on more complex classification challenges. While alluvium serves as an excellent case study for model validation, it does not represent a significant challenge for machine learning techniques compared to difficult lithologies, such as metamorphic formations or volcanic units, where the advantages of advanced algorithms are more justified. Another key finding concerns the high accuracy achieved in classifying trachytes and basalts, despite the challenges inherent in detecting them, especially under dense vegetation cover. In field surveys, trachytes and basalts are often difficult to identify due to surface weathering and vegetation that obscures outcrops. However, the results suggest that the geographic and spectrometric parameters integrated into the model help overcome these challenges. Trachytes have specific spectral signatures, particularly in the near-infrared and mid-infrared bands, which machine learning algorithms can exploit to differentiate them from adjacent formations. Additionally, their rugged topography, characterized by steep slopes, provides a geomorphological cue that aids in their detection.