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Remote sensing mineralization alteration information extraction based on PCA and SVM optimized by cuckoo algorithm

  • role： First author第一作者
  • Affiliation：

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

    Key Laboratory of Metallogeny and Mineral Assessment, Ministry of Land and Resources, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China

    Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China

    Key Laboratory of Geo-Spatial Information Technology, Ministry of Land and Resources, Chengdu University of Technology, Chengdu 610059, China

    Key Laboratory of Mineral Resources in Western China (Gansu Province), Lanzhou University, Lanzhou 730000, China

    Digital Land Key Laboratory of Jiangxi Province, East China Institute of Technology, Nanchang 330013, China

  • Email：nuaaimage@163.com
  • Introduction：吴一全，1963年生，男，教授，研究方向为遥感图像处理、分析与理解，目标检测与识别。E-mail：nuaaimage@163.com
Yiquan WU123456,  
  • Affiliation：

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Donghui SHENG1,  
  • Affiliation：

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Yang ZHOU1

реферат

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.

ключеви́че слова́

remote sensing;mineralization alteration information extraction;principal component analysis;support vector machine;cuckoo algorithm;band ratio method

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