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Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 50 - 62 Titre : Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification Type de document : Article/Communication Auteurs : Congcong Wen, Auteur ; Lina Yang, Auteur ; Xiang Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 50 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] fusion de données [Termes IGN] plus proche voisin, algorithme du [Termes IGN] précision de la classification [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] traitement de semis de points Résumé : (auteur) Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810. Numéro de notice : A2020-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.004 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94743 [article]

A topology-preserving polygon rasterization algorithm / Chen Zhou in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 6 (November 2018) . - pp 495 - 509 Titre : A topology-preserving polygon rasterization algorithm Type de document : Article/Communication Auteurs : Chen Zhou, Auteur ; Dingmou Li, Auteur ; Ningchuan Xiao, Auteur ; Zhenjie Chen, Auteur ; Xiang Li, Auteur ; Manchun Li, Auteur Année de publication : 2018 Article en page(s) : pp 495 - 509 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] données vectorielles [Termes IGN] polygone [Termes IGN] rastérisation [Termes IGN] relation topologique [Termes IGN] traitement de données localisées Résumé : (Auteur) Conventional algorithms for polygon rasterization are typically designed to maintain non-topological characteristics. Consequently, topological relationships, such as the adjacency between polygons, may also be lost or altered, creating topological errors. This paper proposes a topology-preserving polygon rasterization algorithm to avoid topological errors. Four types of topological error may occur during polygon rasterization. The algorithm starts from an initial polygon rasterization and uses a set of preserving strategies to increase topological accuracy. The count of the four types of error measures the topological errors of the conversion. Topological accuracy is summarized as 1 minus the ratio of actual topological errors to the total number of possible error cases. When applied to a land-use dataset with a data volume of 128 MB, 127,836 polygons, and extending 1352 km2, the algorithm achieves a topological accuracy of more than 99% when raster cell size is 30 m or smaller (100% for 5 and 10 m). The effects of cell size, polygon shape, and number of iterations on topological accuracy are also examined. Numéro de notice : A2018-473 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1401488 Date de publication en ligne : 21/11/2017 En ligne : https://doi.org/10.1080/15230406.2017.1401488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91256 [article]

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032-2018061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Progress in marine oil spill optical remote sensing: Detected targets, spectral response characteristics, and theories / Lu yingcheng in Marine geodesy, vol 36 n° 3 (September - November 2013)  

Public [article]  inMarine geodesy > vol 36 n° 3 (September - November 2013) . - pp 334 - 346 Titre : Progress in marine oil spill optical remote sensing: Detected targets, spectral response characteristics, and theories Type de document : Article/Communication Auteurs : Lu yingcheng, Auteur ; Xiang Li, Auteur ; Qingjiu Tian, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 334 - 346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] marée noire [Termes IGN] pollution des mers [Termes IGN] réponse spectrale [Termes IGN] volume (grandeur) Résumé : (Auteur) Different oil spill pollution types could be produced in oil transport and weathering processes. Investigation of these pollution types is beneficial for oil spill recovery and processing. Optical remote sensing techniques play an important role in marine oil spill monitoring and have the ability to identify different oil spill pollution types. Recently, research on oil spill optical remote sensing has made much progress in detecting targets, identifying spectral response characteristics, and formulating theories. Floating black oil, oil slicks, and oil-water mixture in marine oil spill accidents are the main targets to be investigated by optical remote sensors. The visible spectral response differences of these targets are the base of oil spill optical remote sensing research. Bi-directional reflectance distribution function, light interference, absorption, and scattering of targets produce different spectra. Therefore, oil spill optical remote sensing could be used to identify the main oil spill pollution types and estimate oil spill volume. Numéro de notice : A2013-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2013.793633 Date de publication en ligne : 14/12/2009 En ligne : https://doi.org/10.1080/01490419.2013.793633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32849 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
230-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible