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基于影像特征CART决策树的稀土矿区信息提取与动态监测
引用本文:朱青,林建平,国佳欣,郭熙.基于影像特征CART决策树的稀土矿区信息提取与动态监测[J].金属矿山,2019,48(5):161-169.
作者姓名:朱青  林建平  国佳欣  郭熙
作者单位:1. 江西农业大学国土资源与环境学院,江西 南昌 330045;2. 江西省鄱阳湖流域农业资源与生态重点实验室,江西 南昌 330045
基金项目:江西省博士后科研择优资助项目(编号:2015KY23),江西省教育厅科学技术研究重点项目(编号:GJJ170244),江西省重点研发计划A类项目(编号:20181ACG70006)。
摘    要:、裸土指数(Bare Soil Index,BSI)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)3种特征信息进行提取,采用基于CART(Classification and Regression Trees)决策树的分类方法对研究区稀土矿开采信息进行识别,分类总体精度达到89.43%,其中矿区分类精度达到88%,分类精度相对于基于光谱信息的CART决策树分类和最大似然分类有明显提高。通过对研究区2013-2016年稀土矿开采区域进行遥感动态监测,发现增加的开采区域主要分布于矿权范围内,减少的开采区域在矿权界限内外均有大量分布,减少幅度达41%,说明政府和相关矿权部门对于稀土行业健康有序发展发挥了重要作用。研究表明:基于影像特征CART决策树的分类方法在稀土矿区信息提取与动态监测方面具有一定的可行性。

关 键 词:稀土矿区  遥感监测  CART决策树  纹理特征  裸土指数  遥感影像分类  

Information Extraction and Dynamic Monitoring of Rare Earth Mining Area Based on Image Feature CART Decision Tree
ZHU Qing,LIN Jian-Ping,GUO Jia-Xin,GUO Xi.Information Extraction and Dynamic Monitoring of Rare Earth Mining Area Based on Image Feature CART Decision Tree[J].Metal Mine,2019,48(5):161-169.
Authors:ZHU Qing  LIN Jian-Ping  GUO Jia-Xin  GUO Xi
Affiliation:1. Academy of Land Resource and Environment,Jiangxi Agricultural University,Nanchang 330045,China;2. Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province,Nanchang 330045,China
Abstract:In order to accurately reflect the mining status of rare earth mining area in Southern Jiangxi Province,taking Xunwu County of Jiangxi Province as the study area and selecting Landsat-8 multi-spectral image as the data source,through the extraction of the feature information of mean texture,bare soil index (BSI) and normalized difference vegetation index (NDVI),the mining information ore rare earth of the study area is identified by using the classification method based on multi-source data CART decision tree.The results show that the overall accuracy of the classification is 89.43% and the classification precision of the mining area is 88%.The classification accuracy is better than the ones of CART decision tree classification method based on spectral information and maximum likelihood classification method.Based on the above discussion results,remote sensing dynamic monitoring for the rare mining area in the study area from 2013 to 2016 is carried out.It is found that the increasing mining area is mainly distributed within the scope of mining boundary,the reduced mining area is distributed within and outside the mining boundary and the degree of reduction is 41%,which further indicated that the government and related departments have played an important role in developing a healthy and orderly rare earth industry.The above study results show that the classification method based on multi-source data CART decision tree has certain feasibility in information extraction and dynamic monitoring of rare earth mining area.
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