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• W4381617992 startingPage "5807" @default.
• W4381617992 abstract "With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method." @default.
• W4381617992 created "2023-06-23" @default.
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• W4381617992 date "2023-06-21" @default.
• W4381617992 modified "2023-10-13" @default.
• W4381617992 title "Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method" @default.
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• W4381617992 doi "https://doi.org/10.3390/s23135807" @default.
• W4381617992 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37447657" @default.
• W4381617992 hasPublicationYear "2023" @default.
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