MFE-ResNet: A new extraction framework for land cover characterization in mining areas

Obtaining accurate information about land cover is a critical aspect of environmental monitoring in mining areas. However, the land cover in these areas is often complex and poses a challenge for analysis compared to other landscapes. This results in the difficulty of acquiring effective features and an unbalanced proportion of available training samples. We introduce a new approach to obtaining effective image features and enhancing the extraction of open-pit (OP) and waste-dump area (WDA) by using a mining feature-enhanced ResNet (MFE-ResNet). Our framework utilizes image objects as the basic units of high spatial resolution images (HSRIs) to better incorporate spatial and geometric information. The MFE-ResNet is an improvement of the ResNet18 architecture, forming a 26-layer network, and reducing the computational complexity by changing the convolutional kernel size. A squeeze-and-excitation (SE) module is also introduced to form an SE-residual structure to improve the network's ability to perceive the detailed mining area features. To alleviate the sample imbalance problem, a switchable normalization (SN) layer is applied to optimize the internal network structure. In addition, focal loss is introduced in the training process. We build an evaluation system that considers both the number of objects and their area. Compared with other six deep learning methods, our MFE-ResNet achieves the best overall accuracy in both large and small study areas. Specifically, MFE-ResNet obtained F values (F) of 87.73% and 97.28%, and quality (Q) of 81.37% and 95.18% in OP extraction. The F of WDA extraction improved by 0.99% and 2.13%, and the Q improved by 0.98% and 9.56% compared to the second-ranked model. We show that our framework exhibits promising performance in monitoring mining areas.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This study introduces a new approach using a mining feature-enhanced ResNet network to obtain effective image features and enhance the extraction of open-pit and waste-dump areas in mining areas. The results show that the network achieves the best overall accuracy in both large and small study areas.