A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (6), p.2089
• Main Authors: Li, Meng, Tang, Zhuang, Tong, Wei, Li, Xianju, Chen, Weitao, Wang, Lizhe
• Format: Article
• Language: English
• Subjects: deep belief networks; deep learning; fine-scale classification; open-pit mining; remote sensing; Ziyuan-3 imagery
• ISSN: 1424-8220; 1424-8220
• 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.