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摘要： 利用遥感技术对露天开采区进行信息提取和监测已成为解决矿山自然环境问题的重要手段。通过改进带密集连接的全卷积神经网络，构建露天开采区样本库，并训练了针对多源遥感数据的露天开采区提取模型，最终实现对铜陵地区露天开采区的全自动提取。与传统分类方法和深度学习方法相比，该方法在基于像元和基于对象的评价方面具有较好的精度，其中像元精度PA：0.977，交并比IoU：0.721，综合评价指标F1：0.838，Kappa系数：0.825，召回率：0.913，漏警率：0.087，虚警率：0.533。同时，该模型对于匀色较差的GoogleEarth影像也有较好的提取效果，表现出较强的泛化性和适用性，在多源遥感影像露天开采区提取方面具有较强的应用价值。 关键词： 深度学习;  全卷积神经网络;  DenseNet;  露天开采区提取;  全自动化     Abstract: The use of remote sensing technology for information extraction and monitoring of open-pit mining areas has become an important means to solve the natural environment problems of mines. Firstly, this paper improves the fully convolutional neural network with dense block. Then, the opencast mining area sample library is constructed, and the open-pit mining area extraction model for multi-source remote sensing data is trained. Finally, the automatic extraction of the opencast mining area is realized in Tongling. The results show that compared with traditional classification methods and deep learning methods, the proposed method has better accuracy in pixel-based and object-based evaluation. Specifically, the Pixel Accuracy (PA), Intersection over Union (IoU), F1, Kappa Coefficient, Recall, Missing Alarm and False Alarm is 0.977, 0.721, 0.838, 0.825, 0.913, 0.087 and 0.533, respectively. The model also has a great extraction effect for Google-Earth images with poor homogeneity, showing strong generalization and applicability. Therefore, the proposed model of this paper has wide application value in the extraction of opencast mining area by using multi-source remote sensing images.

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