基于岩性指数和三维特征空间的岩性分类方法研究
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1.Chang'2.'3.an University;4.长安大学地球科学与资源学院;5.中国地质调查局西安地质调查中心

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国家自然科学基金项目(面上项目,重点项目,重大项目),


Study on lithologic classification method based on lithologic index and 3D feature space
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1.Chang'2.'3.an University;4.School of Earth Science and Resources,Chang’an University,Xi’an;5.Xi’an Center of Geological Survey,China Geological Survey,Xi’an

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    摘要:

    遥感岩性分类特别是镁铁-超镁铁质岩和花岗岩体自动识别对于矿产勘查具有重要意义,目前尚缺少综合可见光-近红外(VNIR)、短波红外(SWIR)和热红外(TIR)影像的岩性分类方法。本研究以甘肃北山辉铜山和新疆东天山黄山2个区域为研究区,基于先进星载发射与反射辐射计(ASTER)数据,根据岩石在VNIR-SWIR区间的反射光谱特征及TIR区间的辐射特征建立镁铁质-超镁铁质岩指数(MI)、含石英岩指数(QI)和综合比值,利用岩性指数和综合比值结果构建三维特征空间模型,根据岩石在特征空间中的聚类特征实现特定岩性的分类提取。将该模型用于甘肃北山辉铜山和新疆东天山黄山地区岩性分类,野外检查证实分类结果精度较高。结果表明本文提出的综合利用VNIR-SWIR和TIR数据的岩性指数和三维特征空间岩性分类模型,可有效提取目标岩性,具有较高精度和适用性,在我国西部地区具有较好的应用前景。

    Abstract:

    Remote sensing lithologic classification, especially the automatic identification of mafic-ultramafic rocks and granite, is of great significance for mineral exploration. Currently, the lithologic classification methods, which integrate visible light near-infrared (VNIR), short wave infrared (SWIR), and thermal infrared (TIR) images, are relatively lacking. In this study, the Advanced Spaceborne Emission and Reflection Radiometer (ASTER) data were employed for lithologic mapping in the Huitongshan, Beishan, Gansu Province, and Huangshan, East Tianshan, Xinjiang Province. The mafic-ultramafic rock index (MI), quartz-bearing rock index (QI), and the comprehensive ratio were established according to the reflection spectrum characteristics of rocks in the VNIR-SWIR region and the radiation characteristics in the TIR region. The lithology index and comprehensive ratio results were then used to build a three-dimensional feature space model. The specific lithology was classified and extracted according to the clustering characteristics of rocks in the feature space. The model was applied to the lithologic classification of Huitongshan in the Beishan of Gansu Province and Huangshan in the east Tianshan in Xinjiang, field observation confirms that the classification results are of high accuracy. The results showed that the proposed lithologic index and three-dimensional feature space model using VNIR-SWIR and TIR data could successfully extract target lithology with high precision and applicability, which has a good application prospect in western China.

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  • 收稿日期:2023-01-31
  • 最后修改日期:2023-04-27
  • 录用日期:2023-07-02
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