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.
Abstract (translated)
矿业区域的场景分类为地质环境监测和资源开发规划提供了准确的基础数据。本研究融合了多源数据，构建了一个多模态矿地覆盖场景分类数据集。矿区分类的一个重要挑战在于其复杂的空间布局及多尺度特性。通过提取全局与局部特征，可以全面反映空间分布情况，从而更准确地捕捉矿山场景的整体特点。为此我们提出了一种基于协作表征的双分支融合模型，将全局特征分解为一组关键语义向量集。该模型包含三个核心组件： 1. 多尺度全局Transformer支路：利用相邻的大尺度特征生成小尺度特征的全球通道注意力特性，有效捕捉多尺度特征之间的关系。 2. 局部增强协作表征支路：通过局部特性和重构的关键语义集合来优化注意权重，确保矿区的局部背景和详细特性得到有效的整合。这增强了模型对细粒度空间变化的敏感性。 3. 双分支深层特征融合模块：融合两个分支互补的特征以包含更多场景信息。这种融合加强了模型区分并分类复杂矿山景观的能力。 最后，本研究采用多损失计算方法来确保各组件之间的平衡整合。该模型的整体准确率为83.63%，超过了其他比较模型，并且在所有其他评估指标上也取得了最佳表现。
URL
https://arxiv.org/abs/2507.20216
