Lunar Multitype Geological Structure Recognition Based on Cross-View Constraints

The complex and diverse geological structures on the lunar surface serve as a direct record of its long evolutionary history. Comprehensively identifying and classifying these geological structures can not only deepen our understanding of lunar evolution, but also support the planning of future lunar exploration missions and guide the detection of lunar energy resources. However, the diverse and intricate morphologies of lunar geological structures, coupled with certain similarities between different formations, make their automatic identification and classification a significant challenge. To address this issue, we propose a multi-type lunar geological structure recognition network based on cross-view constraints, which mines differentiated feature information to enhance target identification and discrimination capabilities. This network effectively extracts heterogeneous and complementary features through cross-view feature extraction constraints. By employing a discrepancy-weighted loss function, the network focuses on regions where discrepancies arise in the recognition results across multiple views, thereby enhancing attention to divergent areas and learning feature representations in complex scenarios. Additionally, multi-scale contextual information aggregation combines contextual features from different receptive fields, leveraging surrounding terrain to enhance discriminative information for lunar geological structures. Experimental results demonstrate that the proposed method exhibits significant superiority in the task of identifying and classifying multi-type lunar geological structures, with a 1.9% improvement in mIoU.