The widely distributed leucogranite belt in the Himalayan orogeny is expected to have excellent potential for developing rare metal mineralization. Finding a way to effectively map the spatial distribution of leucogranites would be a significant contribution to rare metal exploration. Research shows remote sensing technology has long been recognized the significance in geological works, which greatly promoted mineral exploration in a cost-effective manner, especially in the Himalayan orogenic belt with poor natural environment. However, several challenges still exist in relation to the limited spectral band and spatial resolution of remote sensing images, as well as the onerous data processing. In this context, this study sought to resolve these two issues by applying a hybrid approach that comprises image fusion, metric learning, and random forest methods. For the first challenge, multisource and multisensor remote sensing data were integrated to provide more comprehensive spatial texture characteristics and spectral information. To address the second challenge, this study used a hybrid method of metric learning and random forest to promote computing efficiency and classification accuracy. This process is illustrated through a case study of lithological mapping in Cuonadong dome, the northern part of the Himalayan orogeny belt. Seven target lithological units were effectively discriminated with an 85.75% overall accuracy. This provides an important scientific basis for further exploration for rare metal deposits in the Himalayan orogeny belt, and a way of thinking for detecting geological features under harsh natural conditions.

中文翻译：

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使用度量学习和随机森林的混合方法基于 ASTER 和 Sentinel-2A 数据集绘制喜马拉雅白质花岗岩的地图

喜马拉雅造山带广泛分布的白质花岗岩带有望开发稀有金属矿化。找到一种有效绘制白花岗岩空间分布图的方法将是对稀有金属勘探的重大贡献。研究表明，遥感技术在地质工作中的重要性早已得到认可，极大地促进了矿产勘探的成本效益，尤其是在自然环境恶劣的喜马拉雅造山带。然而，遥感图像有限的光谱带和空间分辨率以及繁重的数据处理仍然存在一些挑战。在这种情况下，本研究试图通过应用包括图像融合、度量学习、和随机森林方法。对于第一个挑战，整合多源和多传感器遥感数据以提供更全面的空间纹理特征和光谱信息。为了解决第二个挑战，本研究使用了度量学习和随机森林的混合方法来提高计算效率和分类精度。这个过程通过喜马拉雅造山带北部错那东穹顶岩性测绘的案例研究来说明。7 个目标岩性单元以 85.75% 的总体准确率被有效区分。这为进一步勘探喜马拉雅造山带稀有金属矿床提供了重要的科学依据，为在恶劣的自然条件下探测地质特征提供了思路。整合多源多传感器遥感数据，提供更全面的空间纹理特征和光谱信息。为了解决第二个挑战，本研究使用了度量学习和随机森林的混合方法来提高计算效率和分类精度。这个过程通过喜马拉雅造山带北部错那东穹顶岩性测绘的案例研究来说明。7 个目标岩性单元以 85.75% 的总体准确率被有效区分。这为进一步勘探喜马拉雅造山带稀有金属矿床提供了重要的科学依据，为在恶劣的自然条件下探测地质特征提供了思路。整合多源多传感器遥感数据，提供更全面的空间纹理特征和光谱信息。为了解决第二个挑战，本研究使用了度量学习和随机森林的混合方法来提高计算效率和分类精度。这个过程通过喜马拉雅造山带北部错那东穹顶岩性测绘的案例研究来说明。7 个目标岩性单元以 85.75% 的总体准确率被有效区分。这为进一步勘探喜马拉雅造山带稀有金属矿床提供了重要的科学依据，为在恶劣的自然条件下探测地质特征提供了思路。