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Auteur Peng Fu |
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Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier / Qimin Cheng in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)
[article]
Titre : Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier Type de document : Article/Communication Auteurs : Qimin Cheng, Auteur ; Yuan Xu, Auteur ; Peng Fu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 295-308 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
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[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] scèneRésumé : (Auteur) Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene classification of remotely sensed images. In this study, a method of scene classification for remotely sensed images based on pretrained densely connected convolutional neural networks combined with an ensemble classifier is proposed to tackle the under-utilization of local and spatial information for image classification. Specifically, we first exploit the pretrained DenseNet and fine-tuned it to release its potential in remote-sensing image feature representation. Second, a spatial-pyramid structure and an improved Fisher-vector coding strategy are leveraged to further strengthen representation capability and the robustness of the feature map captured from convolutional layers. Then we integrate an ensemble classifier in our network architecture considering that lower attention to feature descriptors. Extensive experiments are conducted, and the proposed method achieves superior performance on UC Merced, AID, and NWPU-RESISC45 data sets. Numéro de notice : A2021-334 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.3.295 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.14358/PERS.87.3.295 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97533
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 4 (April 2021) . - pp 295-308[article]Réservation
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