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高光谱数据处理及在铀资源勘查中的应用——以广西苗儿山地区为例
引用本文:刘德长,谢红接,李剑锋,赵英俊,黄树桃,张进业,董济时,陈宝树.高光谱数据处理及在铀资源勘查中的应用——以广西苗儿山地区为例[J].国土资源遥感,1999,10(3):65-71.
作者姓名:刘德长  谢红接  李剑锋  赵英俊  黄树桃  张进业  董济时  陈宝树
作者单位:1. 核工业北京地质研究院,北京,100029
2. 核工业中南地质局,410011
摘    要:以广西苗儿山地区机载成像细分红外高光谱数据为例,探讨了高光谱数据的特点及其处理方法。编制了一系列数据预处理软件,对图像进行了条带去除、波段间配准、航向压缩、镜向变换、辐射校正、正切校正、相对反射率转换等预处理,获得了高质量图像; 在 ENVI软件基础上建立了研究区野外实测光谱曲线库及分类子库,并对各子库进行了系统研究。同时,提出了正确的图像配准方案,对图像进行了精校正和镶嵌,取得了满意的效果; 在纯净像元提取、混合像元分解、匹配滤波、光谱特征拟合等方面进行了探索,提取了该区铀矿化的特征光谱信息; 最后,探讨了高光谱数据的地质应用潜力,尤其在硅化带识别方面的应用取得了成功,发现了一系列的新的含铀硅化断裂带,预测了三个成矿有利地段。经验证,其中两片地区的铀明显偏高并有矿化显示。

关 键 词:高光谱数据  预处理方法  特征信息提取  成矿预测
收稿时间:1999-07-02

HYPERSPECTRAL DATA PROCESSING AND RESEARCH ON GEOLOGICAL APPLICATION IN MIAOERSHAN DISTRICT, GUANGXI PROVINCE
Liu Dechang,Xie Hongjie,Li Jianfeng,Zhao Yingjun,Huang Shutao,Zhang Jinye,Dong Jishi,Chen Baoshu.HYPERSPECTRAL DATA PROCESSING AND RESEARCH ON GEOLOGICAL APPLICATION IN MIAOERSHAN DISTRICT, GUANGXI PROVINCE[J].Remote Sensing for Land & Resources,1999,10(3):65-71.
Authors:Liu Dechang  Xie Hongjie  Li Jianfeng  Zhao Yingjun  Huang Shutao  Zhang Jinye  Dong Jishi  Chen Baoshu
Institution:1. Beijing Research Institute of Uranium Geology, Beijing 100029;
2. Central south Geological Bureau of Nuclear Industry 410011
Abstract:Taking Miaoershan district in Guangxi Province of South China as an example, the characteristics and the processing techniques of the airborne hyperspectral data are discussed in detail. A series of programs for strip_removing, registration between bands, tangent correction illumination calibration and relative reflectance calibration has been developed. On the basis of software ENVI (the Environment for Visualizing Images), a field spectral library in the study area has been set up and a method for geometric corrections with satisfactory mosaic images has been developed. Great achievements in some aspects such as pure pixel extraction, spectral unmixing, interpretation, silicified zones and other alteration zones are successfully delineated and specific uranium prospecting areas are proposed.
Keywords:Hyperspectral data  Processing methods  Characteristic information extraction  Metallogenic prediction
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