<b>Rapid Lithological Mapping Using Multi-Source Remote Sensing Data Fusion and Automatic Sample Generation Strategy</b>

The advancement of remote sensing technology aids geologists in obtaining lithological maps more quickly, comprehensively, and accurately. However, key challenges in lithological mapping include the limited spectral information from individual sensors and the difficulties in visually interpreting lithological samples. In this study, we integrated 241 scenes of optical data and 106 scenes of radar data on the Google Earth Engine (GEE) platform, proposing a rapid lithological identification framework that combines an automatic lithological sample data generation strategy with multi-source data. Using various machine learning algorithms, we evaluated the classification capabilities of heterogeneous predictive factors, feature optimization algorithms, and object-based algorithms. Results indicate that: (1) Combining optical and radar data improves prediction accuracy, with terrain data further enhancing mapping capabilities; (2) Terrain factors contribute most to classification, but SWIR and TIR bands of optical data are critical for lithological identification; (3) The feature optimization algorithm reduces feature redundancy and efficiency issues from multi-source data, achieving 96.51% accuracy with the optimal feature model, an improvement of 0.1%-2.02% over original features; (4) Object-based algorithms show significant potential in mapping areas with large rock outcrops. This study offers new insights for medium- to large-scale lithological maps and provides essential data support for geological work.

遥感技术的进步助力地质学家更快速、全面且精准地获取岩性图。然而，岩性填图仍面临诸多关键挑战：单一传感器的光谱信息有限，且岩性样本的目视解译难度较大。本研究在谷歌地球引擎（Google Earth Engine, GEE）平台上整合了241景光学数据与106景雷达数据，提出了一种将自动岩性样本数据生成策略与多源数据相结合的快速岩性识别框架。研究采用多种机器学习算法，评估了异构预测因子、特征优化算法以及面向对象算法的分类性能。结果表明：（1）光学与雷达数据的融合可提升预测精度，叠加地形数据可进一步增强填图能力；（2）地形因子对分类的贡献度最高，但光学数据的短波红外（Short-Wave Infrared, SWIR）与热红外（Thermal Infrared, TIR）波段是岩性识别的关键；（3）特征优化算法可减少多源数据带来的特征冗余与效率问题，最优特征模型的分类精度可达96.51%，较原始特征提升了0.1%~2.02%；（4）面向对象算法在大面积岩石露头区域的填绘中展现出显著潜力。本研究为中大规模岩性图绘制提供了新的研究思路，同时为地质工作提供了重要的数据支撑。