Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing . vol 87 n° 8Paru le : 01/08/2021 |
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est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)
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Ajouter le résultat dans votre panierDigital building-height preparation from satellite stereo images / P.S. Prakash in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)
[article]
Titre : Digital building-height preparation from satellite stereo images Type de document : Article/Communication Auteurs : P.S. Prakash, Auteur ; Bharath H. Aithal, Auteur Année de publication : 2021 Article en page(s) : pp 557 - 566 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] hauteur du bâti
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle stéréoscopique
[Termes IGN] objet géographique urbain
[Termes IGN] qualité des donnéesRésumé : (Auteur) Buildings are considered prominent objects for understanding the pattern of growth in an urban setting. Remote sensing technology plays a vital role in facilitating data generation pertaining to various urban applications. Digital surface models represent the elevation of the earth surface features, and can be obtained from stereo images, radar, laser scanning, and so on. Photogrammetric techniques applied to optical stereo satellite images are economical and fast ways to generate height information of buildings. In this work, a quantitative and qualitative analysis of digital surface models generated from Cartosat-1 stereo images is compared with openly available data. The study finds that it is possible to acquire about 50 percent of building heights with acceptable error limits. The experimental results indicate that the quality of height information is suitable for applications to assess urban development at a macro scale, but not for individual building-level modeling. Numéro de notice : A2021-669 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.8.557 Date de publication en ligne : 01/08/2021 En ligne : https://doi.org/10.14358/PERS.87.8.557 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98785
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 8 (August 2021) . - pp 557 - 566[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021081 SL Revue Centre de documentation Revues en salle Disponible Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)
[article]
Titre : Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network Type de document : Article/Communication Auteurs : Fengpeng Li, Auteur ; Jiabao Li, Auteur ; Wei Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 577 - 591 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] grande échelle
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] moyenne échelle
[Termes IGN] petite échelle
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set. Numéro de notice : A2021-670 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.8.577 Date de publication en ligne : 01/08/2021 En ligne : https://doi.org/10.14358/PERS.87.8.577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98806
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 8 (August 2021) . - pp 577 - 591[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021081 SL Revue Centre de documentation Revues en salle Disponible