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Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 326 Titre : Extracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area Type de document : Article/Communication Auteurs : Siming Yin, Auteur ; Xian Guo, Auteur ; Jie Jiang, Auteur Année de publication : 2022 Article en page(s) : n° 326 Note générale : résumé Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Streetview [Termes IGN] paysage urbain [Termes IGN] Pékin (Chine) [Termes IGN] segmentation sémantique [Termes IGN] site historique Résumé : (auteur) Accurate extraction of urban landscape features in the historic district of China is an essential task for the protection of the cultural and historical heritage. In recent years, deep learning (DL)-based methods have made substantial progress in landscape feature extraction. However, the lack of annotated data and the complex scenarios inside alleyways result in the limited performance of the available DL-based methods when extracting landscape features. To deal with this problem, we built a small yet comprehensive history-core street view (HCSV) dataset and propose a polarized attention-based landscape feature segmentation network (PALESNet) in this article. The polarized self-attention block is employed in PALESNet to discriminate each landscape feature in various situations, whereas the atrous spatial pyramid pooling (ASPP) block is utilized to capture the multi-scale features. As an auxiliary, a transfer learning module was introduced to supplement the knowledge of the network, to overcome the shortage of labeled data and improve its learning capability in the historic districts. Compared to other state-of-the-art methods, our network achieved the highest accuracy in the case study of Beijing Core Area, with an mIoU of 63.7% on the HCSV dataset; and thus could provide sufficient and accurate data for further protection and renewal in Chinese historic districts. Numéro de notice : A2022-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060326 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.3390/ijgi11060326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100760 [article]

Hyperspectral image noise reduction based on rank-1 tensor decomposition / Xian Guoa in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 50 - 63 Titre : Hyperspectral image noise reduction based on rank-1 tensor decomposition Type de document : Article/Communication Auteurs : Xian Guoa, Auteur ; Xian Guo, Auteur ; Xin Huang, Auteur ; Liangpei Zhanga, Auteur ; Lefei Zhang, Auteur Année de publication : 2013 Article en page(s) : pp 50 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] calcul tensoriel [Termes IGN] décomposition spatiale [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] tenseur [Termes IGN] valeur propre [Termes IGN] voxel Résumé : (Auteur) In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial–spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the proposed HSI noise reduction algorithm. Numéro de notice : A2013-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32626 [article]

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