利用遥感和深度学习技术实现露天矿区土地利用变化信息的自动提取对于矿区开采监测、生态环境保护具有重要意义。针对复杂异质矿区场景下土地利用类型的变化，构建新型深度学习模型FM-UNet++，利用高分二号(GF-2)卫星影像实现露天矿区的自动变化检测。首先，通过资料调查和目视解译制作露天矿区变化检测数据集，通过数据增强对其进行扩充；其次，通过在UNet++模型中引入特征增强坐标注意力机制FECA(Feature-enhanced Coordinate Attention)和Mish激活函数构建面向露天矿区变化检测的FM-UNet++模型；最后，训练FM-UNet++及7种对比模型进行露天矿区变化检测实验，并比较不同深度学习模型的检测结果。结果表明：特征增强坐标注意力机制FECA和Mish激活函数均提升了UNet++模型的露天矿区变化检测性能。FM-UNet++模型对露天矿区变化检测的精确率(Precision)、召回率(Recall)、F1-Score和交并比(IoU)值分别达到95.6%、89.2%、92.3%和85.7%，相较于FCN、PSPNet、Deeplabv3+、LANet、UNet、UNet++和DA-UNet++等深度学习模型均有显著提升。FM-UNet++模型的变化检测速度与上述7种对比模型保持在同一数量级，且其训练过程稳定，验证了FM-UNet++模型应用于露天矿区变化检测的可行性。

Automatic extraction of land use change information in open-pit mines using the remote sensing and deep learning technology is of great significance for the mining monitoring and ecological environmental protection. A novel deep learning model FM-UNet++ was constructed for the change of land use types in complex and heterogeneous mining scenarios, and the automatic change detection of open-pit mines was achieved using the Gaofen-2 (GF-2) satellite images. Firstly, the change detection dataset of open-pit mine was produced through data surveys and visual interpretation, which was augmented by data enhancement. Secondly, the FM-UNet++ for open-pit mine change detection was constructed by introducing the Feature-enhanced Coordinate Attention (FECA) mechanism and the Mish activation function into the UNet++. Finally, FM-UNet++ and 7 comparative models were trained for the change detection experiment of open-pit mine and the detection results of different deep learning models were compared. The results show that: (1) The FECA mechanism and the Mish activation function both improve the performance of the UNet++ for open-pit mine change detection. (2) The precision, recall, F1-Score and IoU of FM-UNet++ model are 95.6%, 89.2%, 92.3% and 85.7% respectively for the change detection of open-pit mine, which is significantly improved compared with the deep learning models such as FCN, PSPNet, Deeplab v3+, LANet, UNet, UNet++ and DA-UNet++. Besides, the change detection speed of the FM-UNet++ model remains in the same order of magnitude as the 7 comparative models mentioned above, with a stable training process, validating the feasibility of applying the FM-UNet++ model to the change detection in open-pit mines.