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DOI码：10.3390/rs15153829
发表刊物：Remote Sensing
关键字：open-pit mining area; extraction; remote sensing; EMANet; FC-CRF
摘要：Rapid and accurate identification of open-pit mining areas is essential for guiding production planning and assessing environmental impact. Remote sensing technology provides an effective means for open-pit mine boundary identification. In this study, an effective method for delineating an open-pit mining area from remote sensing images is proposed, which is based on the deep learning model of the Expectation-Maximizing Attention Network (EMANet) and the fully connected conditional random field (FC-CRF) algorithm. First, ResNet-34 was applied as the backbone network to obtain preliminary features. Second, the EMA mechanism was used to enhance the learning of important information and details in the image. Finally, a postprocessing program based on FC-CRF was introduced to optimize the initial prediction results. Meanwhile, the extraction effect of MobileNetV3, U-Net, fully convolutional network (FCN), and our method were compared on the same data set for the open-pit mining areas. The advantage of the model is verified by the visual graph results, and the accuracy evaluation index based on the confusion matrix calculation. pixel accuracy (PA), mean intersection over union (MIoU), and kappa were 98.09%, 89.48%, and 88.48%, respectively. The evaluation results show that this method effectively identifies open-pit mining areas. It is of practical significance to complete the extraction task of open-pit mining areas accurately and comprehensively, which can be used for production management and environmental protection of open-pit mines.
论文类型：期刊论文
学科门类：工学
一级学科：矿业工程
文献类型：J
卷号：15
期号：15
页面范围：3829
是否译文：否
发表时间：2023-08-01
收录刊物：SCI