昆明理工学科技英语翻译作业.docxVIP

Title：Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery利用多时段高分辨率卫星影像监控露天煤矿地区来源：国际煤炭地质学杂志 2011年见刊作者：NurayDemirel等土耳其安卡拉中东理工大学采矿工程系数据库：ELSEVIERAbstract：Surface mining activities, exploitation of ore and stripping and dumping overburden, cause changes on the land cover and land use of the mine area. 露天采矿活动，矿石的开采和表土的剥离和倾倒，造成矿区土地覆盖和土地使用变化。Sustainable mining requires continuous monitoring of these changes to identify the long-term impacts of mining on environment and land cover to provide essential safety measures.可持续开采需要连续监测这些变化来确定开采对环境和土地覆盖的长期影响提供必要的安全措施。 In this sense, digital image classification provides a powerful tool to obtain a rigorous data and hence diminishes the essence of time-consuming and costly field measurements. 在这个意义上说，数字图像分类提供了一个强大的工具来获得严格的数据而且因此减少实地测量的时间和费用。There are various image classification techniques, serving different features for different purposes, and the Support Vector Machine (SVM) classification method based on statistical machine learning theory stands out to be an effective and accurate image classification technique among them.各种不同的图像分类技术，服务于不同目有不同功能，支持向量机（SVM）的分类方法是基于统计机器学习理论，代表一个有效和准确的图像分类技术。This research study investigates the use of SVM classification methods for identifying, quantifying, and analyzing the spatial response of landscape due to surface mining activities in Goynuk open cast mine, Turkey, from year 2004 to 2008. 本研究探讨土耳其高纳克的露天矿山从2004年至2008年，使用SVM分类方法识别，量化，分析由于地表采矿活动产生的空间响应景观。The research algorithm essentially entails (i) acquiring data, (ii) pre-processing the data, (iii) performing image classification, (iv) accuracy assessment and change detection, and (v) analysis ofresults. The results showed that SVM classification method can effectively be utilized for high spatial resolution multispectral satellite images for identifying the changes in surface coal mine area.研究算法本质上需要（一）数据采集，（二）预处理数据，（三）进行图像分类，（四）准确评估和变化检测（五）分析和结果。结果表明，支持向量机分类方法可以有效地被利用在高空间分辨率多光谱卫星图像确定表面煤矿矿区范围内的变化上。Key wor