Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method
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 pro...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/13/5807 |
| _version_ | 1797590859898683392 |
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| author | Jingwen Li Yanting Cai Xu Gong Jianwu Jiang Yanling Lu Xiaode Meng Li Zhang |
| author_facet | Jingwen Li Yanting Cai Xu Gong Jianwu Jiang Yanling Lu Xiaode Meng Li Zhang |
| author_sort | Jingwen Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:29:27Z |
| format | Article |
| id | doaj.art-7f387d3bedbc4a72b8246c947013c595 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7f387d3bedbc4a72b8246c947013c5952023-11-18T17:27:03ZengMDPI AGSensors1424-82202023-06-012313580710.3390/s23135807Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping MethodJingwen Li0Yanting Cai1Xu Gong2Jianwu Jiang3Yanling Lu4Xiaode Meng5Li Zhang6College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaCollege of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, ChinaWith 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.https://www.mdpi.com/1424-8220/23/13/5807remote sensing image retrievalvisual feature word bagbag-of-wordsassociation rules |
| spellingShingle | Jingwen Li Yanting Cai Xu Gong Jianwu Jiang Yanling Lu Xiaode Meng Li Zhang Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method Sensors remote sensing image retrieval visual feature word bag bag-of-words association rules |
| title | Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method |
| title_full | Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method |
| title_fullStr | Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method |
| title_full_unstemmed | Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method |
| title_short | Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method |
| title_sort | semantic retrieval of remote sensing images based on the bag of words association mapping method |
| topic | remote sensing image retrieval visual feature word bag bag-of-words association rules |
| url | https://www.mdpi.com/1424-8220/23/13/5807 |
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