Accurate extraction of ground objects in mining areas using high spatial resolution imagery can effectively assist in the arrangement and deployment of the production work in mining areas. Therefore, it is essential to extract ground objects of mining areas quickly and accurately. However, the task is very difficult due to the variety of complex scenes, the high intra-class variation, low inter-class variation, similar appearance, and large size difference among the ground objects in mining areas. Traditional extraction methods based on RGB images can no longer meet the requirement of high precision. To solve these problems, we propose an attention-based multi-level feature fusion network (AMFNet), which extracts objects more accurately from unmanned aerial vehicle-based RGB images and digital surface model (DSM). The DSM of the mining area provides the height information of the ground objects, which is another supplementary key feature while the objects cannot be distinguished only by appearance in the RGB images. AMFNet is a dual-branch network of encoder–decoder structure using RGB images and DSM as the double-branch input separately. Specifically, the channel attention module, MF, and atrous convolution are introduced in the encoder network to make better use of dual-branch features. The decoder network upsamples the deep features and uses skip connections to fuse the multi-level shallow features obtained by the encoder network with the deep features. Finally, we set up multiple comparisons and ablation experiments in the test dataset, which demonstrate the performance advantages of AMFNet from different dimensions.

中文翻译：

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AMFNet：一种基于注意力的多级特征融合网络，用于从矿区基于无人机的 RGB 图像和数字表面模型中提取地物

利用高空间分辨率影像准确提取矿区地物，可以有效辅助矿区生产工作的安排和部署。因此，快速准确地提取矿区地物至关重要。然而，由于矿区地物场景多样、类内变异大、类间变异小、外观相似、尺寸差异大，任务难度很大。传统的基于RGB图像的提取方法已经不能满足高精度的要求。为了解决这些问题，我们提出了一种基于注意力的多级特征融合网络 (AMFNet)，它可以从基于无人机的 RGB 图像和数字表面模型 (DSM) 中更准确地提取对象。矿区的 DSM 提供了地物的高度信息，这是另一个补充的关键特征，而仅通过 RGB 图像中的外观无法区分地物。AMFNet 是一种编码器-解码器结构的双分支网络，分别使用 RGB 图像和 DSM 作为双分支输入。具体来说，在编码器网络中引入了通道注意力模块、MF 和 atrous 卷积，以更好地利用双分支特征。解码器网络对深层特征进行上采样，并使用跳跃连​​接将编码器网络获得的多级浅层特征与深层特征融合。最后，我们在测试数据集中设置了多次比较和消融实验，从不同维度展示了 AMFNet 的性能优势。