Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China.
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2021 Mar 16;21(6):2089. doi: 10.3390/s21062089.
Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.
矿区精细土地利用和土地覆盖（LULC）数据有助于对采矿活动进行智能监管。然而，露天矿区的复杂景观严重限制了分类精度。尽管深度学习（DL）算法具有提取信息特征的能力，但它们需要大量的样本数据。因此，设计具有更低样本需求和更高可解释性的 DL 模型非常重要。本研究基于紫源三号影像，开发了一种新颖的基于多级输出的深度置信网络（DBN-ML）模型，用于武汉市露天矿区的精细分类。首先，最后一个 DBN 层用于输出精细尺度的土地覆盖类型。然后，前一个 DBN 层之一输出第一级土地覆盖类型。粗分类更容易，所需的 DBN 层数更少。最后，这两个损失被加权以优化 DBN-ML 模型。由于一级类提供了更多的额外样本数据，且无需额外成本，因此多级输出策略增强了 DBN-ML 模型的鲁棒性。所提出的模型产生了 95.10%的整体精度和 95.07%的 F1 分数，优于其他一些模型。
