Low-Rank Semantic Feature Reconstruction Hashing for Remote Sensing Retrieval

Remote sensing image retrieval (RSIR) is the main technology for automatic analysis and understanding of remote sensing big data, which has been widely concerned in recent years. The mainstream attention mechanism based on high order tensor plays an important role in computer vision, but the model complexity is high. Low-rank feature reconstruction can reconstruct high-order context semantics based on low-rank tensor, and the feature reconstruction layer can realize the fusion of high-order context semantics. In order to reconstruct high-order remote sensing semantics, we propose a novel low-rank semantic feature reconstruction hashing (LRSFRH) using a lightweight dual-attention mechanism and semantic reservation loss to capture remote contextual semantic information of remote sensing scenes for remote sensing retrieval. Its main contributions are as follows: 1) lightweight dual-attention mechanism is proposed based on effective channel attention (ECA) and high-order tensor reconstruction (HTR). Among them, HTR can explore high-order contextual semantic information of remote sensing with low-order constraints, and ECA’s cross-channel interaction can significantly reduce model complexity while maintaining performance; 2) in the remote sensing feature hashing space, we use second-order global covariance pooling (GCP) to accelerate model convergence and enrich remote sensing semantic representation; and 3) in metric learning, we propose a new multiple semantic reconstruction loss (MSRL) to optimize network parameters. Experimental results show that LRSFRH outperforms most existing hash algorithms on two public benchmark datasets (AID and UC Merced), and the proposed algorithm achieves state-of-the-art (SOTA) performance in RSIR tasks.