# Multi-Source Satellite Imagery and Machine Learning
###### Abstract  
Accurate identification of geological formations is essential for understanding tectonic structures, planning mining activities, and sustainably managing natural resources. It goes beyond the scientific framework to play a key role in economic development, environmental preservation, and population security. This article proposes a study using machine learning to analyze different parameters from various sources of satellite imagery: multispectral optics (Landsat-8), radar (ALOS PALSAR), and soil and morphometric parameters (soil, altitude, slope, curvature, and shady). The data were preprocessed to remove atmospheric biases and harmonize spatial resolutions. Techniques such as principal component analysis, band ratios, and image fusion have made it possible to enrich imagery by highlighting spectral and textural characteristics. Finally, classifiers such as Random Forest, Gradient Boosting, and XGBoost (version 1.6.2) were used to evaluate the impact of each parameter on the classification. The results show that geographic parameters combined with PCA provide the best overall performance with Random Forest, achieving an accuracy of 55.29% and an MCC of 45.12% while ensuring a rapid training speed (3.6 s). The geographic parameters associated with the OLI spectrometric data show a good balance, with XGBoost achieving a slightly higher MCC (40.3%) with a moderate training time (7.9 s). On the other hand, the OLI spectrometric parameters coupled with PCA display significantly lower performance, with an accuracy of 45.05% and an MCC of 31.81% for Random Forest. These observations highlight the potential of geographic and geological parameters associated with suitable models to improve classification. The multi-source approach thus proves optimal for more robust and precise results. s 2025 6 12 10.390/signals/12/05/0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-000000-0000000-superposition of soil and plant covers, this task remains challenging in mountainous and densely vegetated regions, such as the Western Highlands of Cameroon [2]. These constraints limit the effectiveness of conventional mapping methods based on field surveys and geophysical observations. However, the emergence of remote sensing technologies and machine learning algorithms provides new opportunities to overcome these obstacles and provide robust and scalable solutions. Multisource satellite data, combining optical images, radar, and digital terrain models (DTMs), enable detailed analysis of geological features, even in complex environments [3]. LANDSAT and ALOS PALSAR images provide complementary perspectives: optical data capture spectral information about mineral composition, while radar data reveal underlying structures through their sensitivity to surface roughness [4]. Additionally, DTMs, such as those provided by the SRTM (Shuttle Radar Topography Mission), facilitate the extraction of morphometric parameters essential for identifying tectonic structures and drainage patterns. Integrating these multisource data with advanced machine learning techniques represents a significant breakthrough in this context [5]. Supervised learning models, such as decision trees and neural networks, allow for capturing complex relationships between environmental, spectral, and morphometric variables, leading to more accurate and automated geological classification. This approach provides a better understanding of local geological systems and a strategic tool for water resource management, sustainable mining, and natural hazard assessment applications. This article proposes an innovative methodology combining multi-source satellite data and machine learning algorithms to detect and map the geological formations of the Western Highlands of Cameroon. The objective is to establish a reproducible and extensible methodological framework capable of responding to the challenges linked to this region's geological complexity and environmental variability.