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Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 15-28 Titre : Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss Type de document : Article/Communication Auteurs : Xianwei Zheng, Auteur ; Linxi Huan, Auteur ; Gui-Song Xia, Auteur ; Jianya Gong, Auteur Année de publication : 2020 Article en page(s) : pp 15-28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification basée sur les régions [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] contour [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation sémantique Résumé : (Auteur) Parsing very high resolution (VHR) urban scene images into regions with semantic meaning, e.g. buildings and cars, is a fundamental task in urban scene understanding. However, due to the huge quantity of details contained in an image and the large variations of objects in scale and appearance, the existing semantic segmentation methods often break one object into pieces, or confuse adjacent objects and thus fail to depict these objects consistently. To address these issues uniformly, we propose a standalone end-to-end edge-aware neural network (EaNet) for urban scene semantic segmentation. For semantic consistency preservation inside objects, the EaNet model incorporates a large kernel pyramid pooling (LKPP) module to capture rich multi-scale context with strong continuous feature relations. To effectively separate confusing objects with sharp contours, a Dice-based edge-aware loss function (EA loss) is devised to guide the EaNet to refine both the pixel- and image-level edge information directly from semantic segmentation prediction. In the proposed EaNet model, the LKPP and the EA loss couple to enable comprehensive feature learning across an entire semantic object. Extensive experiments on three challenging datasets demonstrate that our method can be readily generalized to multi-scale ground/aerial urban scene images, achieving 81.7% in mIoU on Cityscapes Test set and 90.8% in the mean F1-score on the ISPRS Vaihingen 2D Test set. Code is available at: https://github.com/geovsion/EaNet. Numéro de notice : A2020-703 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.019 date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96228 [article]

Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1903 - 1929 Titre : Network-constrained bivariate clustering method for detecting urban black holes and volcanoes Type de document : Article/Communication Auteurs : Qiliang Liu, Auteur ; Zhihui Wu, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1903 - 1929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse bivariée [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] circulation urbaine [Termes descripteurs IGN] contour [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] réseau de contraintes [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] sécurité publique [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] trou noir [Termes descripteurs IGN] voisinage (topologie) [Termes descripteurs IGN] volcan [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Urban black holes and volcanoes are typical traffic anomalies that are useful for optimizing urban planning and maintaining public safety. It is still challenging to detect arbitrarily shaped urban black holes and volcanoes considering the network constraints with less prior knowledge. This study models urban black holes and volcanoes as bivariate spatial clusters and develops a network-constrained bivariate clustering method for detecting statistically significant urban black holes and volcanoes with irregular shapes. First, an edge-expansion strategy is proposed to construct the network-constrained neighborhoods without the time-consuming calculation of the network distance between each pair of objects. Then, a network-constrained spatial scan statistic is constructed to detect urban black holes and volcanoes, and a multidirectional optimization method is developed to identify arbitrarily shaped urban black holes and volcanoes. Finally, the statistical significance of multiscale urban black holes and volcanoes is evaluated using Monte Carlo simulation. The proposed method is compared with three state-of-the-art methods using both simulated data and Beijing taxicab spatial trajectory data. The comparison shows that the proposed method can detect urban black holes and volcanoes more accurately and completely and is useful for detecting spatiotemporal variations of traffic anomalies. Numéro de notice : A2020-511 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1720027 date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1720027 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95665 [article]

Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2412 - 2429 Titre : Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation Type de document : Article/Communication Auteurs : Deliang Xiang, Auteur ; Wei Wang, Auteur ; Tao Tang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2412 - 2429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] chatoiement [Termes descripteurs IGN] contour [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image radar [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] superpixel [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) This article proposes an efficient and adaptive statistical superpixel merging approach with edge penalty for polarimetric synthetic aperture radar (PolSAR) image segmentation. Based on the initial superpixel over-segmentation result obtained by our previously proposed adaptive polarimetric superpixel generation algorithm (Pol-ASLIC), this work achieves efficient and accurate PolSAR image segmentation by merging superpixels using the statistical region merging (SRM) framework. This article proposes to define a new dissimilarity measure between superpixels, which takes the edge penalty into consideration, leading to a reasonable and accurate merging order for superpixel pairs. With regard to the merging predicate of superpixels, a polarimetric homogeneity measurement (HoM) is used to define the merging threshold, making the merging predicate and merging threshold adaptive to the PolSAR image content. Experimental results on three airborne and one spaceborne PolSAR data sets demonstrate that the proposed approach can effectively improve the computation efficiency and segmentation accuracy in comparison with state-of-the-art merging-based methods for PolSAR data. More importantly, the proposed approach is free of parameters and easy to use. Numéro de notice : A2020-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2949066 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2949066 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94864 [article]

Contour lines generation in karstic plateaus for topographic maps / Guillaume Touya (2019)    

Public Titre : Contour lines generation in karstic plateaus for topographic maps Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Hugo Boulze, Auteur ; Anouk Schleich, Auteur ; Hervé Quinquenel , Auteur Congrès : Congrès: ICC 2019, 29th International Cartographic Conference ICA Mapping everything for everyone (15 - 20 juillet 2019; Tokyo, Japon) , Commanditaire Editeur : International Cartographic Association ICA Année de publication : 2019 Autre Editeur : Göttingen : Copernicus publications Collection : Proceedings of the ICA Projets : 1-Pas de projet / Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] contour [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] grotte [Termes descripteurs IGN] Jura, massif du [Termes descripteurs IGN] karst [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] QGIS Résumé : (auteur) Contour lines are a key features of topographic maps as they make the comprehension of terrain more easy. But they are no longer drawn by cartographers, they are mostly automatically derived from digital terrain models. Despite real progress in this automated derivation, some specific terrain landscapes remain incorrectly depicted with such techniques, and this is the case for karstic plateaus full of sinkholes. This paper proposes a specific automated method to derive better contour lines in plateaus, particularly around sinkholes. The process first detects karstic plateaus with many sinkholes, as well as the individual sinkholes. Then, the DTM is smoothed to better reflect the terrain in the plateau and in its surroundings. As a third step, the contour lines around sinkholes are enhanced to draw legible round features that better reflect the real terrain. The process was implemented in a QGIS plugin and tested on a small area with a karstic plateau in the Jura mountain in France, and the cartographers of IGN, the French national mapping agency assessed the results as a great improvement compared to the generic automated process to derive contour lines. Numéro de notice : C2019-011 Affiliation des auteurs : LaSTIG COGIT (2012-2019) Thématique : GEOMATIQUE/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-2-133-2019 date de publication en ligne : 10/07/2019 En ligne : http://dx.doi.org/10.5194/ica-proc-2-133-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93259

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DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification / Hongchao Ma in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 260 - 271 Titre : DEM refinement by low vegetation removal based on the combination of full waveform data and progressive TIN densification Type de document : Article/Communication Auteurs : Hongchao Ma, Auteur ; Weiwei Zhou, Auteur ; Liang Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 260 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de Levenberg-Marquardt [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] contour [Termes descripteurs IGN] décomposition de Gauss [Termes descripteurs IGN] densification [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] filtrage de la végétation [Termes descripteurs IGN] forme d'onde pleine [Termes descripteurs IGN] hauteur de la végétation [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] signal laser [Termes descripteurs IGN] Triangulated Irregular Network Résumé : (Auteur) Filtering of low vegetation with height less than approximately 1.5 m is a challenging problem, especially in mountainous areas covered by heavy low foliage, bushes and sub-shrubberies, etc. The paper proposes an approach for obtaining a more accurate Digital Elevation Model (DEM) by removing low vegetation from point cloud. The approach combines point cloud with full waveform data, and begins by filtering point cloud by way of progressive TIN densification (PTD) method. Ground points are thus extracted, but mixed with false ground points, which are mainly from low vegetation and other manmade low objects. Gaussian decomposition by grouping Levenberg–Marquardt (LM) algorithm with F test is performed for the full waveforms corresponding to the extracted ground points. Echo widths and backscattering coefficients are calculated based on the parameters extracted from the decomposition, and used to discriminate points of low vegetation from points of other low objects, allowing the false ground points reflected from low vegetation to be labeled. New elevation values are calculated from the last echoes of the waveforms from low vegetation, and the DEM is updated by replacing the original elevations with the calculated ones. The resultants are assessed both quantitatively by check points and qualitatively by rendered DEM and contour lines generated from it. The accuracy of the refined DEM with low vegetation removal increases by 31% compared with the original DEM in the experiment, showing the effectiveness of the proposed approach. Numéro de notice : A2018-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.009 date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91553 [article]

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