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Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 172 - 186 Titre : Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] véhicule automobile Résumé : (Auteur) Real-time unmanned aerial vehicles (UAVs)-based thermal infrared images processing, due to high spatial resolution and knowledge of the various infrared radiant energy level distribution of solid bodies, has important applications such as monitoring and control of the various phenomena in different natural situations. One of these applications is monitoring the ground vehicles in cities by using detection or semantic segmentation of them in the thermal images. In this research, our purpose is to improve the performance of deep learning combined model by using Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) specifications for the segmentation of the ground vehicles from UAV-based thermal infrared imagery. The proposed model is studied in three steps. First, designing the proposed model by using an encoder-decoder structure and addition of extracted features from convolutional layers and restricted Boltzmann machine in the network. Second, the implementation of the research goals on four sets of UAV-based thermal infrared imagery named NPU_CS_UAV_IR_DATA that was collected from some streets of China by using FLIR TAU2 thermal infrared sensor in 2017. Finally, analyzing the performance of the proposed model by using five state-of-the-art models in semantic segmentation. The results evaluated the performance of the proposed model as a robust model with the average precision and average processing time of approximately 0.97, and 19.73 s for all datasets, respectively. Numéro de notice : A2019-315 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.009 date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93341 [article]

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Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 70 - 83 Titre : Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours Type de document : Article/Communication Auteurs : David Griffiths, Auteur ; Jan Boehm, Auteur Année de publication : 2019 Article en page(s) : pp 70 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] bati [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données publiques [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] Royaume-Uni [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] zone rurale Résumé : (Auteur) Robust and reliable automatic building detection and segmentation from aerial images/point clouds has been a prominent field of research in remote sensing, computer vision and point cloud processing for a number of decades. One of the largest issues associated with deep learning methods is the high quantity of data required for training. To help address this we present a method to improve public GIS building footprint labels by using Morphological Geodesic Active Contours (MorphGACs). We demonstrate by improving the quality of building footprint labels for detection and semantic segmentation, more robust and reliable models can be obtained. We evaluate these methods over a large UK-based dataset of 24556 images containing 169835 building instances. This is achieved by training several Mask/Faster R-CNN and RetinaNet deep convolutional neural networks. Networks are supplied with both RGB and fused RGB-lidar data. We offer quantitative analysis on the benefits of the inclusion of depth data for building segmentation. By employing both methods we achieve a detection accuracy of 0.92 (mAP@0.5) and segmentation f1 scores of 0.94 over a 4911 test images ranging from urban to rural scenes. Numéro de notice : A2019-265 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.013 date de publication en ligne : 06/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93079 [article]

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Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 246 - 258 Titre : Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Florent Lafarge, Auteur Année de publication : 2019 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] prise en compte du contexte [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] scène [Termes descripteurs IGN] scène intérieure [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Auteur) Analyzing and extracting geometric features from 3D data is a fundamental step in 3D scene understanding. Recent works demonstrated that deep learning architectures can operate directly on raw point clouds, i.e. without the use of intermediate grid-like structures. These architectures are however not designed to encode contextual information in-between objects efficiently. Inspired by a global feature aggregation algorithm designed for images (Zhao et al., 2017), we propose a 3D pyramid module to enrich pointwise features with multi-scale contextual information. Our module can be easily coupled with 3D semantic segmentation methods operating on 3D point clouds. We evaluated our method on three large scale datasets with four baseline models. Experimental results show that the use of enriched features brings significant improvements to the semantic segmentation of indoor and outdoor scenes. Numéro de notice : A2019-271 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.010 date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93089 [article]

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Semantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 98 - 113 Titre : Semantic segmentation of road furniture in mobile laser scanning data Type de document : Article/Communication Auteurs : Fashuai Li, Auteur ; Matti Lehtomäki, Auteur ; Sander J. Oude Elberink, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 98 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] mobilier urbain [Termes descripteurs IGN] segmentation sémantique Résumé : (Auteur) Road furniture recognition has become a prevalent issue in the past few years because of its great importance in smart cities and autonomous driving. Previous research has especially focussed on pole-like road furniture, such as traffic signs and lamp posts. Published methods have mainly classified road furniture as individual objects. However, most road furniture consists of a combination of classes, such as a traffic sign mounted on a street light pole. To tackle this problem, we propose a framework to interpret road furniture at a more detailed level. Instead of being interpreted as single objects, mobile laser scanning data of road furniture is decomposed in elements individually labelled as poles, and objects attached to them, such as, street lights, traffic signs and traffic lights. In our framework, we first detect road furniture from unorganised mobile laser scanning point clouds. Then detected road furniture is decomposed into poles and attachments (e.g. traffic signs). In the interpretation stage, we extract a set of features to classify the attachments by utilising a knowledge-driven method and four representative types of machine learning classifiers, which are random forest, support vector machine, Gaussian mixture model and naïve Bayes, to explore the optimal method. The designed features are the unary features of attachments and the spatial relations between poles and their attachments. Two experimental test sites in Enschede dataset and Saunalahti dataset were applied, and Saunalahti dataset was collected in two different epochs. In the experimental results, the random forest classifier outperforms the other methods, and the overall accuracy acquired is higher than 80% in Enschede test site and higher than 90% in both Saunalahti epochs. The designed features play an important role in the interpretation of road furniture. The results of two epochs in the same area prove the high reliability of our framework and demonstrate that our method achieves good transferability with an accuracy over 90% through employing the training data of one epoch to test the data in another epoch. Numéro de notice : A2019-266 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.001 date de publication en ligne : 08/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93081 [article]

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Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser mobile Résumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075 [article]

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Semantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Semantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)  

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Correcting rural building annotations in OpenStreetMap using convolutional neural networks / John E. Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

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LU-Net, simple approach to 3D LiDAR point cloud semantic segmentation / Pierre Biasutti (2019)    

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Point cloud oversegmentation with graph-structured deep metric learning / Loïc Landrieu (2019)  

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

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Configurable 3D scene synthesis and 2D image rendering with per-pixel ground truth using stochastic grammars / Chenfanfu Jiang in International journal of computer vision, vol 126 n° 9 (September 2018)  

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Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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