Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method.
Sensors (Basel)
; 23(13)2023 Jun 21.
Article
de En
| MEDLINE
| ID: mdl-37447657
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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Sémantique
/
Algorithmes
Type d'étude:
Risk_factors_studies
Langue:
En
Journal:
Sensors (Basel)
Année:
2023
Type de document:
Article
Pays d'affiliation:
Chine