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A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 115 - 136 Titre : A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Zhilong You, Auteur ; Guixi Shen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 115 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification orientée objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion d'images [Termes IGN] image captée par drone [Termes IGN] reconnaissance d'objets [Termes IGN] route [Termes IGN] scène urbaine [Termes IGN] semis de points Résumé : (auteur) Urban management and survey departments have begun investigating the feasibility of acquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/PGVNet. Numéro de notice : A2022-756 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.022 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101759 [article]

Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])  

Public [article]  inGeocarto international > vol 37 n° 22 [10/10/2022] . - pp 6643 - 6670 Titre : Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information Type de document : Article/Communication Auteurs : Ozlem Akar, Auteur ; Esra Tunc Gormus, Auteur Année de publication : 2022 Article en page(s) : pp 6643 - 6670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] texture d'image [Termes IGN] transformation en ondelettes [Termes IGN] Turquie Résumé : (auteur) Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%, 8%, 9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP (area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method. Numéro de notice : A2022-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1944453 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1944453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101675 [article]

Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 13 (July-1 2022) . - n° 3227 Titre : Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 Type de document : Article/Communication Auteurs : Fahime Arabi Aliabad, Auteur ; Hamid Reza Ghafarian Malamiri, Auteur ; Saeed Shojaei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3227 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification orientée objet [Termes IGN] croissance urbaine [Termes IGN] détection de changement [Termes IGN] Google Earth [Termes IGN] image captée par drone [Termes IGN] image Sentinel-MSI [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method. Numéro de notice : A2022-572 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.3390/rs14133227 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.3390/rs14133227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101288 [article]

The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)  

Public [article]  inEcological indicators > vol 139 (June 2022) . - n° 108930 Titre : The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning Type de document : Article/Communication Auteurs : Elie Morin, Auteur ; Pierre-Alexis Herrault, Auteur ; Yvonnick Guinard, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse du paysage [Termes IGN] BD Topo [Termes IGN] carte d'occupation du sol [Termes IGN] carte de la végétation [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] connexité (topologie) [Termes IGN] corridor biologique [Termes IGN] extraction de la végétation [Termes IGN] extraction de traits caractéristiques [Termes IGN] indicateur environnemental [Termes IGN] milieu urbain [Termes IGN] Niort [Termes IGN] planification urbaine [Termes IGN] Poitiers [Termes IGN] segmentation d'image [Termes IGN] Vienne (86) Résumé : (auteur) Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning. Numéro de notice : A2022-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.ecolind.2022.108930 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.108930 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100592 [article]

3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 5 (May 2022) . - pp 1759 - 1774 Titre : 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation Type de document : Article/Communication Auteurs : Heyang Thomas Li, Auteur ; Zachary Todd, Auteur ; Nikolas Bielski, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1759 - 1774 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] chaîne de traitement [Termes IGN] classification orientée objet [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espace image [Termes IGN] extraction de traits caractéristiques [Termes IGN] route [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] signalisation routière Résumé : (auteur) The classification and extraction of road markings and lanes are of critical importance to infrastructure assessment, planning and road safety. We present a pipeline for the accurate segmentation and extraction of rural road surface objects in 3D lidar point-cloud, as well as a method to extract geometric parameters belonging to tar seal. To decrease the computational resources needed, the point-clouds were aggregated into a 2D image space before being transformed using affine transformations. The Mask R-CNN algorithm is then applied to the transformed image space to localize, segment and classify the road objects. The segmentation results for road surfaces and markings can then be used for geometric parameter estimation such as road widths estimation, while the segmentation results show that the efficacy of the existing Mask R-CNN to segment needle-type objects is improved by our proposed transformations. Numéro de notice : A2022-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02103-8 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02103-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100627 [article]

Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Scene understanding and gesture recognition for human-machine interaction / Naina Dhingra (2022)  

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Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])  

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Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])  

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An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

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Analyse de la dynamique d’embroussaillement des pelouses calcaires par traitement d’images / Théo Mesure (2021) 

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Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Détection/reconnaissance d'objets urbains à partir de données 3D multicapteurs prises au niveau du sol, en continu / Younes Zegaoui (2021)  

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Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)

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Object-based classification of mixed forest types in Mongolia / E. Nyamjargal in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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