%0 Journal Article
%T 基于光谱匹配的热红外高光谱数据岩性分类研究<br>An Improved Lithological Classification Method for Thermal Infrared Hyperspectral Data Based on Spectral Matching
%A 孙娅琴
%A 田淑芳
%A 王兴振
%A 高雅洁&lt
%A br&gt
%A SUN Ya-qin
%A TIAN Shu-fang
%A WANG Xing-zhen
%A GAO Ya-jie
%J 现代地质
%D 2016
%X 摘要： 从岩石光谱出发，结合光谱谱带强度特征和光谱波形特征，针对机载热红外高光谱数据（TASI），在以往算法基础上，提出一种改进的算法——光谱离散能级波形匹配法（SDEM），并将其运用到岩性分类研究中。SDEM算法能识别岩石光谱间的微小差异，并在充分考虑光谱谱带强度和波形特征的同时，有效减弱数据噪声。与传统的岩性分类方法——高光谱角度制图法（SAM）相比，改进的算法能更精确地区分岩石相似光谱，识别易混淆岩性，对出现“异物同谱”现象的岩石也具有更好的区分能力。将SDEM、SAM方法应用于甘肃柳园地区TASI数据岩性分类研究中，可看出SDEM方法能识别出SAM未识别或识别错误的岩性。通过研究区野外查证，可知SDEM方法所得岩性分类结果更符合岩石实际分布情况。可见光谱离散能级波形匹配法具有较好的岩性分类效果，能更好地区分地物。<br>Abstract: Feature spectral characteristics are the base of hyperspectral remote sensing technology. Based on rock spectral characteristics, for the purpose of classifying lithology by using Thermal Infrared Airborne Hyperspectral Imager (TASI) data, an improved lithological classification algorithm-spectral divergence energy-level matching (SDEM)-is presented in this paper. SDEM can identify tiny differences between any two different spectra. Also, this method takes both spectral band intensity and spectral waveform into account, and can effectively reduce the impact of image noises. Compared with the traditional lithological classification method-high spectral angle mapping (SAM), the improved algorithm can distinguish those similar but different spectra more precisely, and can identify those easily confused lithology. This method is also good at distinguishing the lithology known as “different features with similar spectra”. Using the TASI data of Liuyuan region in Gansu Province, we compared the lithological classification results of SDEM and SAM methods, and found that the SDEM method can identify the lithology that SAM can’t identify or wrongly identified. Based on our field validation work, the classification result by SDEM is more accordant with the actual distribution of rock, and is also more detailed
%K 热红外高光谱
%K 光谱特征
%K 光谱离散能级波形匹配法
%K 甘肃柳园地区
%K 岩性分类
%K thermal infrared hyperspectra
%K spectral characteristic
%K spectral divergence energy-level matching (SDEM)
%K Liuyuan region in Gansu Province
%K lithological classification
%U http://www.geoscience.net.cn/CN/abstract/abstract13778.shtml