收稿：2017-03-21，
录用：2017-8-16，
纸质出版：2018-09
移动端阅览
为了进一步提高遥感矿化蚀变信息提取的精度，本文提出了一种基于主成分分析PCA (Principal Component Analysis)和布谷鸟算法优化支持向量机SVM (Support Vector Machine)的遥感矿化蚀变信息提取方法。首先，通过波段比值法增强研究区遥感图像中的矿化蚀变信息，并获得比值图像；然后，对比值图像进行主成分分析，进而提取训练样本；接着，利用SVM对训练样本进行训练，同时采用布谷鸟算法求取SVM的最优核参数及惩罚因子，构造最优SVM模型；最后，运用最优SVM模型完成矿化蚀变信息提取。选择青海省五龙沟地区为研究区，提取羟基及铁染蚀变信息。实验结果表明，与主成分分析法、基于光谱角法和SVM的方法、基于粒子群和SVM的方法及基于波段比值、PCA和粒子群优化SVM的方法等4种方法相比，本文方法获得的遥感矿化蚀变信息和已知矿点的吻合度最高，提取效果最好。
With the rapid development of the economy
the demand for mineral resources is growing
and the contradiction between supply and demand is increasing. The shortage of mineral resources has become one of the important factors that restrict national economic development. Therefore
research on how to efficiently and accurately explore mineral resources is a critical endeavor. Remote sensing mineralization alteration information extraction is an important application of remote sensing technology in geological exploration
which is of utmost significance to mineral exploration and evaluation. Owing to the influence of vegetation
cloud
and snow
alteration information from remote sensing mineralization is often superimposed with the complex geological background and exists only in the form of a weak signal in the background of the remote sensing image. Research on effective remote sensing mineralization alteration information extraction methods can provide the basis for the study of regional metallogenic prognosis and speed up the evaluation of mineral resources exploration
which helps promote the healthy and stable development of the local mining economy. To improve the accuracy of remote sensing mineralization alteration information extraction method
a remote sensing mineralization alteration information extraction method based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) optimized by cuckoo algorithm is proposed in this study. First
the mineralization alteration information in the remote sensing image of the study area is enhanced by band ratio method
and the ratio images are obtained. Then PCA is applied to the ratio images of the study area. The hydroxyl principal components and iron staining principal components are selected
after which the training samples are extracted. Subsequently
the training samples are trained by SVM
while cuckoo algorithm is used to find the optimal kernel parameter and penalty factor of SVM. Thus
the optimal SVM model is determined. Finally
the optimal SVM model is used to accomplish the extraction of remote sensing mineralization alteration information in the study area. Wulonggou area of Qinghai Province
which is rich in mineral resources
is selected as the study area where the hydroxyl alteration information and iron alteration information are extracted. A detailed comparison among the proposed method and four methods proposed recently
namely
the PCA method
the method based on spectral angle mapper and SVM
the method based on particle swarm optimization and SVM
and the method based on band ratio
PCA
and SVM optimized by particle swarm optimization in terms of extraction effect and matching rate
is given in this paper. Experimental results show that by using the proposed method
the extracted information can comprehensively reflect the remote sensing mineralization alteration information of the study area. Moreover
the matching degree of hydroxyl alteration information and iron alteration information are 86.5% and 69.2%
respectively. Meanwhile
compared with the four methods
the proposed method can obtain the highest matching degree with the best extraction effect. The proposed remote sensing mineralization alteration information extraction method based on PCA and SVM optimized by cuckoo algorithm is an effective method that provides a new idea for mineral exploration and metallogenic prediction.
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