摘要:
从遥感蚀变异常主分量图像中提取异常信息,目前主要基于数据的正态分布特征,并未考虑地质异常的非线性特征.针对此问题,提出了分维变点提取算法(fractal dimension-change point method,FDCPM).首先利用分形模型计算蚀变异常的自相似性参数值,再采用变点模型计算蚀变异常的突变性参数值,然后确定蚀变异常临界阈值,达到有效区分地质背景和蚀变异常的目的.以甘肃北山方山口地区为研究区,对识别的ASTER蚀变异常进行测试和验证,并对其提取精度进行初步评价和比较.结果表明: 对于实验中的褐铁矿、绢云母和绿泥石3种蚀变矿物来说,分维变点法的总体提取精度略高于门限化方法.利用分维变点法提取3种蚀变矿物的正确率均超过83%,且遥感蚀变异常的分布与化探及重砂异常有较高的吻合度,已知金属矿(化)点也几乎都落在遥感蚀变异常区内或其边缘,表明分维变点法行之有效,可作为今后划分地质背景和蚀变异常的方法之一.
Abstract:
At present, the extracting method for remote sensing alteration anomalies from principal component image relies mainly on the data's normal distribution, without considering the nonlinear characteristics of geological anomaly.To tackle this problem, the authors have proposed the fractal dimension-change point method(FDCPM)in this paper.By calculating the self-similarity and mutability of alteration anomalies with fractal dimension-change point model, the critical threshold of an alteration anomaly was acquired quantitatively.The realization theory and access mechanism of the method were elaborated by an experiment with ASTER data in Fangshankou,Beishan,and the results of the proposed method and traditional method (de-interfered anomalous principal component thresholding technique,DIAPCTT) were compared with each other.The results show that the FDCPM has a relatively high extracting precision than the DIAPCTT for three alteration minerals in the experiment.In this experiment, the accuracy of three alteration minerals could reach over 83%.Moreover, the distribution of remote sensing alteration anomalies agrees well with a large amount of evidence from the geochemical anomaly and the heavy sand anomaly.What's more, the known polymetallic ore spots and mineralized spots fall in the zone of remote sensing alteration anomaly or at its edge.All the results mentioned above show that the FDCPM is one of the effective distinguishing methods for the geological background and the remote sensing alteration anomaly in the future.
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