Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval

Understand

Remote sensing (RS) cross-modal text-image retrieval has attracted extensive attention for its advantages of flexible input and efficient query.

• However, traditional methods ignore the characteristics of multi-scale and redundant targets in RS image, leading to the degradation of retrieval accuracy.
• To cope with the problem of multi-scale scarcity and target redundancy in RS multimodal retrieval task, we come up with a novel asymmetric multimodal feature matching network (AMFMN).
• Our model adapts to multi-scale feature inputs, favors multi-source retrieval methods, and can dynamically filter redundant features.