A Stepwise Framework for Fine-Scale Mining Area Types Recognition in Large-Scale Scenes by GF-5 and GF-2 Images

Abstract

Quickly obtaining fine-scale mining area types information in large-scale scenes is significant for dynamically detecting mineral resources. Currently, mining area types recognition methods encounter challenges such as low recognition accuracy and difficulty detecting small mining areas. To address these issues, this article proposes a stepwise top-down mining area types recognition framework. The framework consists of two steps. First, a GF-5 spectral index named the Normalized Difference Mining Area Index (NDMAI) is constructed to obtain the rough position of the mining area quickly. Then, the identification network of Mine Types with Transformer (Mitformer) is proposed for accurate type recognition of the candidate mining area regions. Mitformer combines a multiscale feature enhancement module and a decoder based on multilevel skip connections, which achieves a sufficient fusion of features at each layer of deep feature maps and adds the skip connections between low-level and high-level feature maps, thus, can improve the accuracy of types identification and the detection rate of small-scale mining areas. Moreover, this framework can effectively avoid misclassification caused by different objects with similar spectra to the maximum extent possible. This article selects two independent study areas with a large spatial extent, respectively, in Hebei Province and Anhui Province. The imagery utilized for these regions is obtained from Chinese GF-2 and GF-5 satellites. Multiple experiments are conducted to verify the superiority of NDMAI and Mitformer and the effectiveness of this framework. The experimental results illustrate that this framework can provide adequate technical support for the dynamic detection of mineral resources.

Keywords

• GF-5 spectral index
• large-scale scenes
• mining area identification
• mining area types
• semantic segmentation