HiReNet: Hierarchical-Relation Network for Few-Shot Remote Sensing Image Scene Classification
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Le résumé fourni par la source
Few-shot scene classification aims to develop models that can quickly adapt to new scenes with only a few labeled samples that are not present in training sets. In recent years, convolutional neural networks (CNNs) have made significant advancements in few-shot remote sensing image scene classification tasks. However, most existing approaches focus solely on utilizing high-level embeddings of remote sensing images to learn similarity relations, while neglecting intrinsic hierarchical representations that could be crucial in distinguishing scenes with substantial interclass similarities. To address this limitation, we propose a novel few-shot scene classification method for remote sensing images called hierarchical-relation network (HiReNet). This approach leverages the hierarchical features of a query sample and its corresponding support sample to learn discriminative representations. HiReNet consists of an embedding network and a relation network. The embedding network employs a Siamese architecture to extract representations, while the relation network utilizes these representations for classification. Within the relation network, we introduce a hierarchical relation learning (HRL) structure to capture the hierarchical relations among query and support samples. Additionally, to extract stronger features, we introduce a feature aggregation module that concatenates multilevel features and employs channel attention to re- weight these features. Experimental results demonstrate the superior performance of our HiReNet compared to several state-of-the-art few-shot scene classification methods.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- HiReNet: Hierarchical-Relation Network for Few-Shot Remote Sensing Image Scene Classification
- Date Crossref
- 01/01/2024
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Beihang University pays non établi dans la noticeUniversité ou école supérieure
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Southwest Jiaotong University pays non établi dans la noticeUniversité ou école supérieure
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Beijing Academy of Artificial Intelligence pays non établi dans la noticeInstitution
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Shanghai Artificial Intelligence Laboratory pays non établi dans la noticeStructure de recherche
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AVIC Chengdu Aircraft Industrial (Group) Company Ltd. pays non établi dans la noticeEntreprise
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School of Astronautics Department of Guidance pays non établi dans la noticeUniversité ou école supérieure
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School of Information Science and Technology pays non établi dans la noticeUniversité ou école supérieure
Beihang University, Southwest Jiaotong University et Beijing Academy of Artificial Intelligence, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.