Deep global semantic structure-preserving hashing via corrective triplet loss for remote sensing image retrieval

• Published in: Expert systems with applications Vol. 238; p. 122105
• Main Authors: Zhou, Hongyan, Qin, Qibing, Hou, Jinkui, Dai, Jiangyan, Huang, Lei, Zhang, Wenfeng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Asymmetric learning; Corrective triplet loss; Deep hashing; Global contextual information; Remote sensing image retrieval; Swin transformer; Remote sensing image retrieval; Swin transformer; Global contextual information; Corrective triplet loss; Deep hashing; Asymmetric learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.122105

With the explosive increase of remote sensing data, how to search for remote sensing data quickly and accurately in a vast dataset is an incredibly critical matter for research subjects. The deep hashing method has become the dominant method for remote sensing image retrieval because of its low-cost storage and high-speed retrieval. However, for the reason of the limitation of fixed convolutional kernels, deep hashing frameworks based on Convolutional Neural Networks (CNNs) fail to obtain the global semantic information well, which leads to the generation of suboptimal solutions. Furthermore, existing hashing methods commonly employ the random sampling strategy or hardest sample mining to build training batches, resulting in bad local minima. To remedy these problems, a novel Deep Global Semantic Structure-preserving Hashing framework via corrective triplet loss (DGSSH) is proposed for remote sensing image retrieval to learn a discriminative and stable embedding space, achieving intra-class confusion and inter-class diversity. Specifically speaking, the feature extraction module based on Swim Transformer architecture is developed to capture global semantic information and multiscale features from remote sensing images. Based on a distribution matching constraint, the corrective triplet loss for deep hashing schemes is designed to reduce the distribution shift caused by the random selection or hardest sample mining. Meanwhile, to reduce the time overhead of the model, the asymmetric learning strategy is employed to perform effective compact representation learning. Numerous experiments have been carried out on three publicly available benchmarks, which indicates that the proposed DGSSH framework could achieve optimal performance for remote sensing image retrieval applications. The source code of our DGSSH framework is hosted at https://github.com/QinLab-WFU/DGSSH.git. •The Swim Transformer architecture is developed to capture global semantic features.•The corrective triplet loss is proposed to reduce the distribution shift.•An asymmetric learning strategy is employed to improve training efficiency.•The state-of-the-art results are reported on three benchmark datasets.