Dual-Stream Global-Local Feature Collaborative Representation Network for Scene Classification of Mining Area

2025/07/27 by Fan, Shuqi, Haoyi Wang, Wang, Haoyi +2
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing and 3D Reconstruction

paper · pdf · doi:10.48550/arxiv.2507.20216

openalex publication_date 2025/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Scene classification of mining areas provides accurate foundational data for geological environment monitoring and resource development planning. This study fuses multi-source data to construct a multi-modal mine land cover scene classification dataset. A significant challenge in mining area classification lies in the complex spatial layout and multi-scale characteristics. By extracting global and local features, it becomes possible to comprehensively reflect the spatial distribution, thereby enabling a more accurate capture of the holistic characteristics of mining scenes. We propose a dual-branch fusion model utilizing collaborative representation to decompose global features into a set of key semantic vectors. This model comprises three key components:(1) Multi-scale Global Transformer Branch: It leverages adjacent large-scale features to generate global channel attention features for small-scale features, effectively capturing the multi-scale feature relationships. (2) Local Enhancement Collaborative Representation Branch: It refines the attention weights by leveraging local features and reconstructed key semantic sets, ensuring that the local context and detailed characteristics of the mining area are effectively integrated. This enhances the model's sensitivity to fine-grained spatial variations. (3) Dual-Branch Deep Feature Fusion Module: It fuses the complementary features of the two branches to incorporate more scene information. This fusion strengthens the model's ability to distinguish and classify complex mining landscapes. Finally, this study employs multi-loss computation to ensure a balanced integration of the modules. The overall accuracy of this model is 83.63%, which outperforms other comparative models. Additionally, it achieves the best performance across all other evaluation metrics.

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