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Double adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)
[article]
Titre : Double adaptive intensity-threshold method for uneven Lidar data to extract road markings Type de document : Article/Communication Auteurs : Chengming Ye, Auteur ; Hongfu Li, Auteur ; Ruilong Wei, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639-648 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction du réseau routier
[Termes IGN] filtre adaptatif
[Termes IGN] lidar mobile
[Termes IGN] méthode robuste
[Termes IGN] semis de points
[Termes IGN] seuillage de points
[Termes IGN] signalisation routièreRésumé : (Auteur) Due to the large volume and high redundancy of point clouds, there are many dilemmas in road-marking extraction algorithms, especially from uneven lidar point clouds. To extract road markings efficiently, this study presents a novel method for handling the uneven density distribution of point clouds and the high reflection intensity of road markings. The method first segments the point-cloud data into blocks perpendicular to the vehicle trajectory. Then it applies the double adaptive intensity-threshold method to extract road markings from road surfaces. Finally, it performs an adaptive spatial density filter based on the density distribution of point-cloud data to remove false road-marking points. The average completeness, correctness, and F measure of road-marking extraction are 0.827, 0.887, and 0.854, respectively, indicating that the proposed method is efficient and robust. Numéro de notice : A2021-672 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00099 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.20-00099 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98834
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 639-648[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021091 SL Revue Centre de documentation Revues en salle Disponible Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
[article]
Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Brésil
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Sensitivity of change-point detection and trend estimates to GNSS IWV time series properties / Khanh Ninh Nguyen in Atmosphere, vol 12 n° 9 (September 2021)
[article]
Titre : Sensitivity of change-point detection and trend estimates to GNSS IWV time series properties Type de document : Article/Communication Auteurs : Khanh Ninh Nguyen, Auteur ; Annarosa Quarello , Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier Article en page(s) : n° 1102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] coordonnées GNSS
[Termes IGN] données météorologiques
[Termes IGN] incertitude géométrique
[Termes IGN] segmentation
[Termes IGN] série temporelle
[Termes IGN] teneur intégrée en vapeur d'eauRésumé : (auteur) This study investigates the sensitivity of the GNSSseg segmentation method to change in: GNSS data processing method, length of time series (17 to 25 years), auxiliary data used in the integrated water vapor (IWV) conversion, and reference time series used in the segmentation (ERA-Interim versus ERA5). Two GNSS data sets (IGS repro1 and CODE REPRO2015), representative of the first and second IGS reprocessing, were compared. Significant differences were found in the number and positions of detected change-points due to different a priori ZHD models, antenna/radome calibrations, and mapping functions. The more recent models used in the CODE solution have reduced noise and allow the segmentation to detect smaller offsets. Similarly, the more recent reanalysis ERA5 has reduced representativeness errors, improved quality compared to ERA-Interim, and achieves higher sensitivity of the segmentation. Only 45–50% of the detected change-points are similar between the two GNSS data sets or between the two reanalyses, compared to 70–80% when the length of the time series or the auxiliary data are changed. About 35% of the change-points are validated with respect to metadata. The uncertainty in the homogenized trends is estimated to be around 0.01–0.02 kg m−2 year−1. Numéro de notice : A2021-791 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/atmos12091102 En ligne : https://doi.org/10.3390/atmos12091102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98993
in Atmosphere > vol 12 n° 9 (September 2021) . - n° 1102[article]Pattern-based identification and mapping of landscape types using multi-thematic data / Jakub Nowosad in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)
[article]
Titre : Pattern-based identification and mapping of landscape types using multi-thematic data Type de document : Article/Communication Auteurs : Jakub Nowosad, Auteur ; Tomasz F. Stepinski, Auteur Année de publication : 2021 Article en page(s) : pp 1634 - 1649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] gestion des ressources
[Termes IGN] gestion foncière
[Termes IGN] matrice de co-occurrence
[Termes IGN] modèle mathématique
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] paysage
[Termes IGN] régionalisation (segmentation)
[Termes IGN] regroupement de données
[Termes IGN] segmentation en régionsRésumé : (auteur) Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence (JSD), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. Numéro de notice : A2021-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1893324 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1893324 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98064
in International journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1634 - 1649[article]Single annotated pixel based weakly supervised semantic segmentation under driving scenes / Xi Li in Pattern recognition, vol 116 (August 2021)
[article]
Titre : Single annotated pixel based weakly supervised semantic segmentation under driving scenes Type de document : Article/Communication Auteurs : Xi Li, Auteur ; Huimin Ma, Auteur ; Sheng Yi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 107979 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Semantic segmentation tasks based on weakly supervised conditions have been put forward to achieve a lightweight labeling process. For simple images that only include a few categories, research based on image-level annotations has achieved acceptable performance. However, when facing complex scenes, since image contains a large number of classes, it becomes challenging to learn visual appearance based on image tags. In this case, image-level annotations are not useful in providing information. Therefore, we set up a new task in which a single annotated pixel is provided for each category in a whole dataset. Based on the more lightweight and informative condition, a three step process is built for pseudo labels generation, which progressively implements each class’ optimal feature representation, image inference, and context-location based refinement. In particular, since high-level semantics and low-level imaging features have different discriminative abilities for each class under driving scenes, we divide categories into “object” or “scene” and then provide different operations for the two types separately. Further, an alternate iterative structure is established to gradually improve segmentation performance, which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process. Experiments on the Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation tasks under complex driving scenes. Numéro de notice : A2021-985 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.patcog.2021.107979 En ligne : https://doi.org/10.1016/j.patcog.2021.107979 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101354
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