Interpreting surface geological elements (such as rocks, minerals, soils, and water bodies) is the main task of geological survey, which plays a crucial role in geological environment remote sensing (GERS). However, the characteristics of geological elements, including high variabilities, various morphology, complicated boundaries and imbalanced class distribution, make it still a challenge for deep learning methods to interpret GERS images. Considering the correlations of geological elements as the regionalized variables in geostatistics, the sensitive features of GERS interpretation mainly include three aspects: tonal, textural and structural characteristics within a singular-class elements, spatial and spectral correlations of adjacent elements, and their global tectonic or spatial distribution. Thus, to simulate the manual interpretation process of geologists from local to global and promote GERS interpretation performance, we propose a local-to-global multi-scale feature fusion network (LGMSFNet). A geological object context represents the intra-class semantic dependencies of pixel sets with the same class. And a local feature aggregation module models the channel and spatial association. Then discriminative features are integrated by a global feature fusion module. For the model optimization, we focus on hard examples during the training process to achieve the balanced optimization of various categories. Two research areas that include large-scale rocks, soils and water exposed on the surface are selected. Massive experiments demonstrate the superiority of the LGMSFNet in GERS interpretation.

中文翻译：

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通过本地到全球敏感特征融合网络进行地质遥感解释


解释地表地质要素（如岩石、矿物、土壤和水体）是地质调查的主要任务，在地质环境遥感 （GERS） 中起着至关重要的作用。然而，地质要素的高变异性、形态多样、边界复杂、类分布不均衡等特点，使得深度学习方法解释 GERS 图像仍然是一个挑战。将地质要素的相关性作为地统计学中的区域化变量，GERS解释的敏感特征主要包括3个方面：单类要素内的色调、织构和结构特征，相邻要素的空间和光谱相关性，以及它们的整体构造或空间分布。因此，为了模拟地质学家从局部到全局的人工解释过程并促进 GERS 解释性能，我们提出了一种局部到全球多尺度特征融合网络 （LGMSFNet）。地质对象上下文表示具有相同类的像素集的类内语义依赖关系。本地特征聚合模块对通道和空间关联进行建模。然后，通过全局特征融合模块集成判别特征。对于模型优化，我们在训练过程中专注于硬示例，以实现各个类别的均衡优化。选择了两个研究领域，包括暴露在表面的大规模岩石、土壤和水。大量实验证明了 LGMSFNet 在 GERS 解释中的优越性。