An Efficient Remote Sensing Cross-Modal Retrieval Method Based on Hashing Contrastive Learning.
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| Title: | An Efficient Remote Sensing Cross-Modal Retrieval Method Based on Hashing Contrastive Learning. |
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| Authors: | Fang, Jifei1,2,3 (AUTHOR), Zhu, Dali1,2 (AUTHOR) zhudali@iie.ac.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1630. 18p. |
| Subjects: | Contrastive learning, Binary codes, Optimization algorithms, Image retrieval, Remote sensing, Information retrieval |
| Abstract: | Highlights: What are the main findings? A Cross-modal Contrastive Hashing framework is proposed for efficient remote sensing image–text retrieval. Vision–language semantic representations are transferred into compact binary hash codes. What are the implication of the main finding? A collaborative optimization strategy jointly constrains real-valued features and hash representations. The proposed method achieves competitive retrieval accuracy with substantial retrieval-speed and storage-efficiency gains. Cross-modal image–text retrieval enables searching and retrieving of semantically relevant data across heterogeneous modalities, acting as a pivotal technology for interpreting massive remote sensing (RS) data. Despite recent progress, most existing methods in remote sensing cross-modal image–text retrieval (RSCIR) rely on high-dimensional real-valued embeddings, which suffer from excessive storage overhead and slow retrieval speeds, severely limiting their scalability in real-world applications. Conversely, while hashing techniques offer efficiency, traditional methods often fail to preserve the fine-grained semantic consistency required for complex RS scenes, leading to significant performance degradation. To bridge this gap, this paper proposes a novel framework named ConHash (Cross-modal Contrastive Hashing), which transfers the discriminative power of pre-trained vision–language models into a compact binary Hamming space. Specifically, ConHash comprises three synergistic components: (1) a hash module designed to project continuous embeddings into a latent discrete space while reducing information loss; (2) a hash-aware contrastive constraint that enforces cross-modal alignment directly in the hash space; and (3) a collaborative hybrid optimization strategy that jointly constrains real-valued embeddings and hash representations. Extensive experiments on RSICD and RSITMD demonstrate that ConHash achieves a favorable balance between accuracy and efficiency. Using 512-bit hash codes with L1 quantization loss as the main configuration, ConHash achieves mR values of 21.69% and 35.79% on RSICD and RSITMD, respectively. It also provides up to 3.50× retrieval speedup and a 32× theoretical storage reduction compared with 512-dimensional float32 embeddings, making it suitable for scalable remote sensing retrieval applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A Cross-modal Contrastive Hashing framework is proposed for efficient remote sensing image–text retrieval. Vision–language semantic representations are transferred into compact binary hash codes. What are the implication of the main finding? A collaborative optimization strategy jointly constrains real-valued features and hash representations. The proposed method achieves competitive retrieval accuracy with substantial retrieval-speed and storage-efficiency gains. Cross-modal image–text retrieval enables searching and retrieving of semantically relevant data across heterogeneous modalities, acting as a pivotal technology for interpreting massive remote sensing (RS) data. Despite recent progress, most existing methods in remote sensing cross-modal image–text retrieval (RSCIR) rely on high-dimensional real-valued embeddings, which suffer from excessive storage overhead and slow retrieval speeds, severely limiting their scalability in real-world applications. Conversely, while hashing techniques offer efficiency, traditional methods often fail to preserve the fine-grained semantic consistency required for complex RS scenes, leading to significant performance degradation. To bridge this gap, this paper proposes a novel framework named ConHash (Cross-modal Contrastive Hashing), which transfers the discriminative power of pre-trained vision–language models into a compact binary Hamming space. Specifically, ConHash comprises three synergistic components: (1) a hash module designed to project continuous embeddings into a latent discrete space while reducing information loss; (2) a hash-aware contrastive constraint that enforces cross-modal alignment directly in the hash space; and (3) a collaborative hybrid optimization strategy that jointly constrains real-valued embeddings and hash representations. Extensive experiments on RSICD and RSITMD demonstrate that ConHash achieves a favorable balance between accuracy and efficiency. Using 512-bit hash codes with L1 quantization loss as the main configuration, ConHash achieves mR values of 21.69% and 35.79% on RSICD and RSITMD, respectively. It also provides up to 3.50× retrieval speedup and a 32× theoretical storage reduction compared with 512-dimensional float32 embeddings, making it suitable for scalable remote sensing retrieval applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101630 |
