Adaptive Global-local Fusion Network Based Deep Unsupervised Hashing for Remote Sensing Image Retrieval

Unsupervised hashing methods have gained wide-spread popularity for remote sensing (RS) image retrieval due to their high efficiency. Existing methods heavily rely on similarity matrix generated by pre-trained models as supervised signals. However, such pre-trained models obtained from natural images fail to comprehensively extract features in RS images, yielding unreliable similarity relationships. Considering complex features in RS images, we propose a novel adaptive global-local fusion network based deep unsupervised hashing (AFDUH) method. The advantages of AFDUH lie in the use of large kernel convolution and bi-level routing attention mechanism for learning local and global features simultaneously. AFDUH fuses these features under different resolutions in an interactive fashion to deduce high-quality similarity matrix based on contrastive learning. Besides, we embed a sample selection strategy in AFDUH, which can filter out unreliable supervised signals to further improve retrieval accuracy. Extensive experiments on two RS datasets demonstrate that AFDUH outperforms the state-of-the-art baselines.