首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >CROSS CORRELOGRAM SPECTRAL MATCHING - APPLICATION TO SURFACE MINERALOGICAL MAPPING BY USING AVIRIS DATA FROM CUPRITE, NEVADA
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CROSS CORRELOGRAM SPECTRAL MATCHING - APPLICATION TO SURFACE MINERALOGICAL MAPPING BY USING AVIRIS DATA FROM CUPRITE, NEVADA

机译:跨Correlogram光谱匹配-通过使用内华达州CUPRITE的AVIRIS数据将其应用于表面矿物学

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摘要

A new approach toward mineral mapping from imaging spectrometer data is presented, using a spectral matching algorithm based on the cross correlogram. A cross correlogram is constructed by calculating the cross correlation at different match positions, m, between a test spectrum (i.e., a pixel spectrum) and a reference spectrum (i.e., a laboratory mineral spectrum or a pixel spect-nlm Known to represent a mineral of interest) by shifting the reference spectrum over subsequent channel positions. The cross correlogram for perfectly matching reference and test spectra is a parabola around the central matching number (m = 0) with a peak correlation of 1. In laboratory spectra, deviations from this shape indicate differences in mineralogy, whereas, in image data, this may be partly attributed to spectral mixing, noise, changes in atmospheric and illumination conditions, and other scene and sensor-dependent variables. A cross correlogram spectral matching algorithm was designed and tested on 1994 data from the airborne visible/infrared imaging spectrometer of the Cuprite mining area. Accurate mapping of kaolinite, alunite, and buddingtonite was achieved by extracting three parameters from the cross correlograms that were constructed on a pixel-by-pixel basis: the correlation coefficient at match position. zero, the moment of skewness (based on the correlation differences between match numbers of equal but reversed signs; e.g., m = 4 and m = -4), and the significance (based on a Student's t-test of the validity of the correlation coefficients). (C) Elsevier Science Inc., 1997. [References: 24]
机译:提出了一种使用基于交叉相关图的光谱匹配算法从成像光谱仪数据进行矿物映射的新方法。通过计算测试光谱(即像素光谱)和参考光谱(即实验室矿物光谱或已知代表矿物的像素光谱)之间不同匹配位置m处的互相关来构造互相关图通过在随后的信道位置上移动参考频谱来实现。完全匹配的参考光谱和测试光谱的交叉相关图是中心匹配数(m = 0)周围的抛物线,其峰值相关性为1。在实验室光谱中,与该形状的偏差表示矿物学上的差异,而在图像数据中,可能部分归因于频谱混合,噪声,大气和光照条件的变化以及其他取决于场景和传感器的变量。设计了一种交叉相关图谱匹配算法,并根据1994年来自Cuprite矿区的机载可见/红外成像光谱仪的数据进行了测试。通过从以像素为基础构建的互相关图中提取三个参数,可以精确映射高岭石,亚矾石和芽孢石,这是匹配位置的相关系数。零,偏度矩(基于相等但相反符号的匹配数之间的相关性差异;例如,m = 4和m = -4)和显着性(基于学生的t检验,相关性的有效性系数)。 (C)Elsevier Science Inc.,1997年。[参考:24]

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