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Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) . - pp 2248 - 2269 Titre : Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid Type de document : Article/Communication Auteurs : Zhen Dai, Auteur ; Sensen Wu, Auteur ; Yuanyuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2248 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] modèle de régression [Termes IGN] régression géographiquement pondérée [Termes IGN] régression linéaire [Termes IGN] réseau neuronal convolutif Résumé : (auteur) Geographically weighted regression (GWR) is a classical method of modeling spatially non-stationary relationships. The geographically neural network weighted regression (GNNWR) model solves the problem of the inaccurate construction of spatial weight kernels using a spatially weighted neural network. However, when the spatial distribution of observations is uneven, the spatial proximity expression in the input of GWR and GNNWR models does not fully represent the impact of the whole research space on the estimating point. Therefore, we established a global spatial proximity grid (GSPG) to express the spatial proximity of each estimating point and proposed a spatially weighted convolutional neural network (SWCNN) to extract the relationship between the GSPG and spatial weights. Finally, we proposed a geographically convolutional neural network weighted regression (GCNNWR) model combining SWCNN and ordinary linear regression (OLR) model to estimate spatial non-stationarity. We used two case studies of simulated data and real environment data to demonstrate the advancements of the GCNNWR model. The GCNNWR model achieved higher estimation accuracy and greater predictive power than the OLR, GWR, multi-scale GWR (MGWR), and GNNWR models. Moreover, the GCNNWR model maintained its better stability and accuracy in estimating spatially non-stationary relationships when the distribution of observations was uneven. Numéro de notice : A2022-773 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2100892 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2100892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101954 [article]

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Robust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4239 - 4251 Titre : Robust object-based multipass InSAR deformation reconstruction Type de document : Article/Communication Auteurs : Jian Kang, Auteur ; Yuanyuan Wang, Auteur ; Marco Körner, Auteur ; Xiao Xiang Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 4239 - 4251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification orientée objet [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] inversion [Termes IGN] pont [Termes IGN] surveillance géologique [Termes IGN] tenseur Résumé : (Auteur) Deformation monitoring by multipass synthetic aperture radar (SAR) interferometry (InSAR) is, so far, the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on the optimal retrieval of deformation parameters on the basis of a single pixel or a pixel cluster. Only until recently, the first demonstration of object-based urban infrastructure monitoring by fusing InSAR and the semantic classification labels derived from optical images was presented by Wang et al. Given such classification labels in the SAR image, we propose a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise. Our approach does not assume the stationarity of each sample in the object, which is usually assumed in other pixel cluster-based methods, such as SqueeSAR. In addition, to handle outliers in real data, a robust phase recovery step prior to parameter retrieval is also introduced. In typical settings, the proposed method outperforms the current pixelwise estimators, e.g., periodogram, by a factor of several tens in the accuracy of the linear deformation estimates. Last but not least, for a practical demonstration on bridge monitoring, we present a full workflow of long-term bridge monitoring using the proposed approach. Numéro de notice : A2017-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2684424 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2684424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86422 [article]

Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring / Yuanyuan Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 14 - 26 Titre : Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring Type de document : Article/Communication Auteurs : Yuanyuan Wang, Auteur ; Xiao Xiang Zhu, Auteur ; Bernhard Zeisl, Auteur ; Marc Pollefeys, Auteur Année de publication : 2017 Article en page(s) : pp 14 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] données 4D [Termes IGN] fusion d'images [Termes IGN] géométrie de l'image [Termes IGN] image à résolution métrique [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] pont [Termes IGN] semis de points [Termes IGN] surveillance d'ouvrage [Termes IGN] voie ferrée [Termes IGN] zone urbaine Résumé : (Auteur) Using synthetic aperture radar (SAR) interferometry to monitor long-term millimeter-level deformation of urban infrastructures, such as individual buildings and bridges, is an emerging and important field in remote sensing. In the state-of-the-art methods, deformation parameters are retrieved and monitored on a pixel basis solely in the SAR image domain. However, the inevitable side-looking imaging geometry of SAR results in undesired occlusion and layover in urban area, rendering the current method less competent for a semantic-level monitoring of different urban infrastructures. This paper presents a framework of a semantic-level deformation monitoring by linking the precise deformation estimates of SAR interferometry and the semantic classification labels of optical images via a 3-D geometric fusion and semantic texturing. The proposed approach provides the first “SARptical” point cloud of an urban area, which is the SAR tomography point cloud textured with attributes from optical images. This opens a new perspective of InSAR deformation monitoring. Interesting examples on bridge and railway monitoring are demonstrated. Numéro de notice : A2017-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2554563 En ligne : https://doi.org/10.1109/TGRS.2016.2554563 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83949 [article]