Hybrid data fusion mostly gives a better diagnosis to lithological units compared to single-source mapping techniques. Rock unit discrimination depends mainly on variations in the concentrations of chemical elements. Remote sensing datasets reflect these variations as different spectral reflectances, while gamma-ray spectrometric measurements enable recording the varied concentrations of K, Th, and U in these rock units. Accordingly, in this study, we use Support-Vector Machine (SVM) learning algorithm to classify combined high spectral resolution Sentinel 2 data with K, Th, and U content of the rocks to better differentiate a lithologically complex area in Egypt. SVM classifier has been trained and tested on a reference map (built from FCCs, principal and independent component analysis of remote sensing images, as well as previous geological maps) to allocate 13 lithological targets. K, Th, U, and total count maps are interpolated using the inverse distance weighted (IDW) method, cubically resampled, and fused with Sentinel 2 data. We concluded that incorporating any single chemical concentration in the allocation gives better results than using remote sensing data solely and raised the Overall Accuracy by 4.14%, 5.11%, and 6.83% by adding U, K, and Th, respectively. Moreover, blending the total count band (K + Th + U) with Sentinel 2 data outstandingly boosts the classification accuracy by 7.77 %. We performed field reconnaissance to verify the classification results. The study demonstrates the effectiveness of integrating Sentinel 2 data with airborne geophysical spectrometric data, and the proposed approach may prove a more precise and sophisticated lithological map.

与单源成图技术相比，混合数据融合主要为岩性单元提供更好的诊断。岩石单元区分主要取决于化学元素浓度的变化。遥感数据集将这些变化反映为不同的光谱反射率，而伽马射线光谱测量能够记录这些岩石单元中 K、Th 和 U 的不同浓度。因此，在本研究中，我们使用支持向量机 (SVM) 学习算法对组合的高光谱分辨率哨兵 2 数据与岩石的 K、Th 和 U 含量进行分类，以更好地区分埃及的岩性复杂区域。SVM 分类器已在参考地图上进行训练和测试（由 FCC 构建，遥感图像的主成分和独立成分分析，以及以前的地质图）分配 13 个岩性目标。K、Th、U 和总计数图使用反距离加权 (IDW) 方法进行插值，三次重新采样，并与 Sentinel 2 数据融合。我们得出的结论是，在分配中加入任何单一的化学浓度比单独使用遥感数据会得到更好的结果，并且通过添加 U、K 和 Th 分别将总体准确度提高了 4.14%、5.11% 和 6.83%。此外，将总计数带 (K + Th + U) 与 Sentinel 2 数据混合可显着提高分类准确度 7.77%。我们进行了实地勘察以验证分类结果。该研究证明了将 Sentinel 2 数据与机载地球物理光谱数据相结合的有效性，