Fine-Grained Hashing for High Spatial Resolution Optical-SAR Remote Sensing Image Retrieval

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Abstract
Optical (OPT) and Synthetic Aperture Radar (SAR) cross-modal remote sensing image retrieval is a key technique for all-weather Earth observation. The enhancement of SAR image resolution has driven the development from conventional scene-level retrieval to object-level retrieval. Inter-class similarity, intra-class variability, and cross-modal discrepancies are general challenges in cross-modal image retrieval. Although existing methods have attempted to address them at the scene level, these challenges persist in object-level tasks due to inherent limitations of the methods, such as detail loss caused by global pooling and insufficient local discrimination. To address this gap, a novel FGHN is proposed to enhance the retrieval accuracy. First, a focus-aware residual network is designed to highlight discriminative local features, facilitating the extraction of semantic details. Second, an intra-class feature aggregation loss is introduced to improve intra-class compactness, reinforcing the consistency of fine-grained representations. Finally, a distance enhanced weighting strategy is incorporated into the hash learning loss to promote similarity among hash codes of the same class. To support fine-grained OPT-SAR retrieval, we construct a High-Resolution OPT-SAR dataset, termed HROS. It is worth noting that the SAR images in HROS were collected by our team. Extensive experiments are conducted to evaluate both FGHN and HROS. The experimental results demonstrate that HROS exhibits high resolution, strong robustness, and superior differentiation, establishing it as a promising benchmark for OPT-SAR retrieval. FGHN achieves state-of-the-art performance on HROS, obtaining 89.80% mAP and 93.95% P@10 for OPT image retrieval with SAR queries at 32 bits hash code. Its strong generalization capability is further validated on the public CBRSIR VS dataset. Ablation studies confirm the effectiveness of the three innovative modules. Moreover, FGHN proves potential for large-scale remote sensing image retrieval applications.
Funding Information
  • China Scholarship Council (202106210056)

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