Integrating Pseudo-Supervision and Spatial Constraints for Efficient Clustering of Multimodal Remote Sensing Data

Multimodal remote sensing (RS) clustering synergistically integrates multi-dimensional information, effectively addressing the representational limitations of single-modality data. This integration offers essential technical support for fine-grained recognition and accurate interpretation of ground objects in complex scenarios. However, existing methods still face several challenges, including insufficient utilization of spatial information, limited ability to extract consistent information due to inter-modal heterogeneity, and low efficiency when handling large-scale and complex datasets. To address these issues, we propose an Integrating Pseudo-Supervision and Spatial Constraints for Efficient Clustering (PSSC) of Multimodal Remote Sensing Data model. The proposed method begins by constructing spatial bipartite graphs from multimodal data to fully exploit spatial information while reducing computational complexity. These graphs are then stacked into a third-order tensor, upon which a robust denoised representation is learned to suppress noise and preserve the core structural characteristics of the multimodal inputs. Based on this clean tensor, PSSC captures cross-modal consistency by minimizing the tensor nuclear norm within the low-rank space. To further enhance clustering efficiency and accuracy, a region homogeneity-constrained rapid label generation strategy is proposed, which leverages high-confidence pseudo-supervision information from homogeneous regions to iteratively refine clustering labels, thereby significantly reducing computational overhead. Extensive experiments on real-world multimodal datasets validate the effectiveness and superior performance of the proposed method.