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摘要： 矿产资源的过度开发会对自然环境造成严重的负影响，矿山环境监测对生态文明建设具有十分重要意义。在目前的矿山环境监测中，机器学习算法被广泛的使用并取得了较为良好的效果。近年来，随着深度学习领域的快速发展，相关理论知识也逐渐被应用于遥感图像处理中。将深度学习算法与面向对象的思想相结合，以高分二号影像作为研究数据，使用卷积神经网络对河南省禹州市的采矿区进行了以露天采场为主的开发占地类型信息提取，并与支持向量机方法进行对比，最终得到卷积神经网络的总体精度为91.85%，Kappa系数为0.90，均高于支持向量机方法，提取结果也与实际更加相符。表明该方法在露天采场提取中的优势和可行性，可为矿区的环境监测和科学管理提供可靠的技术支撑。 关键词： 露天采场;  面向对象;  卷积神经网络;  深度学习;  矿产资源     Abstract: The overexploitation of mineral resources will have a serious negative impact on the natural environment. The monitoring of the mine environment is of great significance to the construction of ecological civilization. Machine learning algorithms have been widely used in traditional mine monitoring and have achieved good results. In recent years， with the rapid development of the field of deep learning， relevant theoretical knowledge has gradually been applied to remote sensing image processing. In this study， the deep learning algorithm is combined with the object-oriented method， and the GF-2 image is used to extract the land occupation type by applying the conventional neural network from the mining area in Yuzhou City， Henan Province. To compare the performance of the proposed methods， the support vector machine method was used. The results show that the overall accuracy of the convolutional neural network is 91.85% and the kappa coefficient is 0.90， which is higher than the support vector machine method. This paper shows the advantages and feasibility of this method in the extraction of open-pit mining areas and provides reliable technical support for the scientific management and environmental monitoring of open-pit mining areas.

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