Abstract

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.

Highlights

  • It is well known that land use and land cover mapping and its consequences for eco-environmental impact on Earth has been increasingly critical for sustainable development

  • The results show an optimal weight combination of 0.2 for the first-level classes and 0.8 for the second-level classes, with an overall accuracy (OA) of 94.85% ± 0.17%

  • To improve the classification performance and enhance the generalization of DBNbased methods in fine land use and land cover (LULC) areas, a deep belief network (DBN)-ML model was proposed in this study

Read more

Summary

Introduction

It is well known that land use and land cover mapping and its consequences for eco-environmental impact on Earth has been increasingly critical for sustainable development. The mining area and surrounding cropland, forestland, and other surface environmental elements are considered to constitute a complex geological environment. In these areas, a series of geological environmental problems may occur [4,5]. A series of geological environmental problems may occur [4,5] They can cause land degradation [1,2,3,4,5], groundwater pollution, decreased vegetation cover, soil pollution, and geological disasters [6,7,8]. The land use and land cover (LULC) related to open-pit mining is the key in these complex environment areas. In open-pit mining areas, the land use and land cover (LULC)

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call