The Application of Spectral Characteristic Space Method to the Alteration Information Extraction of Lake Superior-Type Iron Deposit
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摘要: 由于境外铁矿床位置偏远, 现场研究困难, 旨在探讨苏必利尔湖型铁矿的高光谱遥感蚀变信息提取方法, 为境外找矿提供依据.鉴于传统蚀变信息提取方法的局限性, 研究了在二维光谱特征空间中提取蚀变信息的方法.高光谱影像经主成分变换后, 包含较多地物信息的两个主成分的二维光谱特征空间体现了各向异性的特点, 不同地物的聚类常呈类似椭圆分布.在蚀变信息的聚类椭圆中圈定散点, 并应用波谱特征拟合, 将异常散点的平均光谱与美国地质调查局(United States Geological Survey, USGS)光谱库中的矿物光谱匹配, 确定蚀变矿物类型.以苏必利尔湖型铁矿——巴西Aguas Claras铁矿区为例, 提取了赤铁矿、绿泥石等蚀变矿物, 结果表明矿物的类型和分布与该区地质概况吻合.
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关键词:
- 光谱特征空间 /
- 蚀变矿物 /
- 二维散点图 /
- 遥感 /
- Hyperion数据
Abstract: The field study of overseas iron ore deposits is difficult because of its remote location. The spectral characteristic was used to extract alteration information in view of the limitation of the traditional methods in this paper, aiming to explore alteration information extraction from hyperspectral images for the Lake Superior type iron ore to facilitate overseas prospecting. A two-dimensional spectral characteristic space takes on an anisotropic feature in associated distribution of two principal bands after principal component analysis. The distribution is usually combined by oval clusters. The scatter points were enclosed in oval clusters of alteration information, and the mean spectra of abnormal scatter points were matched with mineral spectra from United States Geological Survey(USGS) spectral library by means of spectral feature fitting to ensure the type of alteration minerals. Spectral characteristic space method was described with the instance of Lake Superior-type iron deposit—Aguas Claras iron area in Brazil. Hematite, chlorite and other minerals were extracted in this study. It is found that the type and distribution of extracted altered minerals were consistent well with geological condition.-
Key words:
- spectral characteristic space /
- alteration minerals /
- 2D scatter plot /
- remote sensing /
- Hyperion data
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图 3 异常散点平均光谱与USGS光谱对比
虚线代表USGS光谱库中的光谱，实线代表影像中异常散点的平均光谱
Fig. 3. The comparison of mean spectra of abnormal information and mineral spectra from USGS
表 1 Hyperion数据主成分特征贡献值
Table 1. Principal component from Hyperion data
主成分 Band 19 Band 20 Band 21 Band 54 Band 55 Band 203 Band 204 Band 205 Band 206 PC1 0.132 8 0.146 8 0.450 1 0.539 9 0.294 3 0.305 2 0.302 5 0.278 8 0.343 1 PC2 -0.009 8 -0.110 9 0.865 9 -0.090 8 -0.168 0 -0.187 4 -0.244 3 -0.242 5 -0.218 5 PC3 -0.314 0 -0.334 0 -0.160 0 0.746 7 -0.073 3 -0.034 1 -0.069 0 -0.170 9 -0.407 7 PC4 0.437 1 0.715 0 -0.062 8 0.211 9 -0.184 6 -0.086 7 -0.013 5 -0.030 5 -0.454 1 PC5 0.263 3 0.070 8 -0.122 9 0.231 3 0.161 1 -0.037 1 -0.629 2 -0.450 5 0.480 5 PC6 0.029 4 -0.027 5 0.012 7 -0.203 8 0.530 4 0.650 6 -0.208 1 -0.134 3 -0.436 6 PC7 -0.080 8 0.013 7 0.007 6 0.027 6 -0.663 8 0.572 5 -0.355 6 0.293 4 0.107 1 PC8 -0.556 4 0.381 6 0.041 2 0.013 0 0.288 9 -0.253 3 -0.440 6 0.447 9 -0.020 5 PC9 -0.553 4 0.438 0 0.033 5 -0.042 9 -0.119 4 0.215 4 0.289 9 -0.570 6 0.169 3 
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