EO-1 Hyperion数据的预处理、特征提取和岩性填图研究

EO-1 Hyperion传感器是第一个可以获取可见光与近红外以及短波红外波长范围光谱信息的星载高光谱传感器。本文以美国最早的金矿采矿区之一,加利福尼亚州东南巧克力山的Rainbow金矿区作为研究案例,探讨了Hyperion数据的预处理方法,专题信息提取与填图,评估了Hyperion高光谱数据在识别与金矿有关的岩性类型的应用价值。结果表明,本文所提出的Hyperion数据预处理方法是有效的,MNF方法能有效用于Hyperion数据维数的降低和数据冗余的去除以及分类特征的提取。最大似然分类器能够有效地从Hyperion高光谱数据中提取与金矿相关的重要岩体信息,所得到的岩性单元与地质图上对应的岩性分布具有很好的一致性。岩体分类的总精度为86%。该研究表明,Hyperion高光谱数据能够很好识别有细微光谱差别的岩性,因而在地质学研究与找矿领域有着良好的应用前景。

Preprocessing, Feature Extraction and Lithologic Mapping Using EO-1 Hyperion Data

EO-1 Hyperion sensor is the first spaceborne hyperspectral instrument to acquire both VNIR and SWIR spectra. A Hyperion image which covers the middle part of the southeastern Chocolate Mountains district and the Rainbow mine area was used to evaluate the utility of Hyperion imagery in identifying gold-related rock units in this area. A group of pre-Tertiary gneiss appears to be the favorable host of gold deposits in the district. Satellite mapping of gold-associated lithologic units is helpful for the search for gold and understanding the regional geology. Pre-processing aspects of Hyperion data before it can be used for information extraction were first addressed in the study. The data dimension was reduced for data redundancy removal and feature extraction. A supervised classifier was finally used to extract the lithologic units that are significant for gold exploration in the study area. The result shows that MLC classifier is efficient for the extraction of lithologic units from the Hyperion data in the study area, and the resultant rock units have excellent correlation with those on the geologic reference map. The MLC classifier creates a "hard" classification with an overall accuracy of 86% and a Kappa coefficient 0.81. This case study indicates that Hyperion data can achieve accurate mapping of gold-associated rock units in the southeastern Chocolate Mountains.