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含矿热液蚀变带是成矿作用发生的重要标志之一,它作为矿产资源快速评价和定位预测的一个主要参数,在遥感地质找矿中具有重要意义。本文在分析干旱区ETM遥感影像地物识别所依据的光谱特征的基础上,提出一种基于小波自适应增强及主分量分析相结合的蚀变遥感异常信息提取方法,结合MATLAB编程软件和ERDAS8.6遥感图像处理软件进行蚀变信息提取试验,并分别与传统的基于波段选择的主成分提取法以及基于主成分分析和比值增强相结合的提取法进行对比分析和效果查证。实验结果表明,研究区内利用小波自适应增强与主分量分析相结合提取的遥感异常与已知矿床、矿(化)点的吻合率达到80%,新发现矿化点一处和外围找矿线索多处,能取得较为满意的提取效果,具有快速、高效、经济的特点,较之传统方法具有很大的优越性。
The ore-bearing hydrothermal alteration zone is one of the most important indicators of mineralization. It is one of the main parameters for rapid evaluation and location prediction of mineral resources, and is of great significance in the prospecting of remote sensing geology. Based on the analysis of the spectral characteristics of ETM remote sensing image feature recognition in arid area, this paper proposes a method of extracting remote sensing anomaly information based on wavelet adaptive enhancement and principal component analysis, combined with MATLAB programming software and ERDAS8. 6 remote sensing image processing software for alteration information extraction experiments, and compared with the traditional method of principal component extraction based on band selection and the extraction method based on principal component analysis and ratio enhancement, respectively. The experimental results show that the coincidence rate of remote sensing anomaly extracted by wavelet adaptive enhancement and principal component analysis in the study area reaches 80% with known ore deposits and mineralization points. One newly discovered mineralization point and the periphery of the ore prospecting Clues many, can obtain more satisfactory extraction effect, with fast, efficient and economical features, compared with the traditional method has great superiority.