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摘要： 在地球科学领域中,自相似性非常普遍,围岩蚀变作为一种地学特征并以遥感异常反映在影像上,其像元亮度—面积模式符合一定的分形理论。利用分形理论的求和法进行遥感蚀变异常的提取和分级可以弥补门限法和灰度直方图目视方法确定突变点的不足,再次利用分形的盒维数方法可得到研究区遥感蚀变异常的分形特征。以云南昭通毛坪铅锌矿ETM+遥感影像数据为信息源,采用主成分分析法(PCA)计算出反映矿区遥感蚀变异常的主分量,再对异常主分量进行像元灰度值和像元个数的统计并采用分形方法对蚀变异常信息进行分析和蚀变异常级别的划分,最后利用分形的盒维数法得到毛坪铅锌矿遥感蚀变异常信息的分形特征并与传统门限化方法进行了对比研究。研究表明区内遥感蚀变异常信息具有统计上的分形特征,分形方法确定异常限要比传统的门限化方法选取蚀变阈值更为准确;分维值与异常级别存在一定的正相关关系;分维高值区与蚀变信息密集区相一致。  关键词： 遥感;  蚀变异常;  分形;  毛坪铅锌矿     Abstract: In the field of earth science,Self\|similarity is very common.The wall rock alteration is reflected on the image as a geo\|science character,whose pixel brightness and area model conform to the fractal theory.Using the summation method of fractal theory for the extraction of remote sensing alteration anomalies and classification can make up for the threshold method and gray level histogram visual method to determine the mutation point.Finally obtaining the fractal features of remote sensing alteration anomaly in the study area by fractal box dimension method.This paper taks ETM+ remote sensing image data of Maoping lead\|zinc deposit as the information source,which is used for Principal Component Analysis (PCA) reflect the mining area of remote sensing alteration anomaly of principal components,and statistics the principal component of pixel grey value and the number of pixels.Then fractal method is used to analyze alteration anomaly information and divide the alteration abnormal levels.Finally using fractal box dimension method get Maoping lead\|zinc deposit fractal features of remote sensing alteration anomaly information and compared with the traditional threshold method.The study show that the remote sensing alteration information of the study area has characteristics of statistical fractal and the fractal method to determine abnormal limit is more accurate than the traditional methods of threshold selection alteration threshold;There is a positive correlation between fractal dimension values and the abnormal level;Fractal dimension of high value areas and the alteration information concentration areas is consistent. 

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