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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)
[article]
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 routièreRésumé : (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
in The Visual Computer > vol 38 n° 5 (May 2022) . - pp 1759 - 1774[article]Human cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([01/05/2022])
[article]
Titre : Human cognition based framework for detecting roads from remote sensing images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Himadri Vaidya, Auteur ; Jayanta Kumar Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 2365 - 2384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image numérique
[Termes IGN] classification
[Termes IGN] cognition
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] interprétation (psychologie)
[Termes IGN] représentation cognitive
[Termes IGN] segmentation d'imageRésumé : (auteur) The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct road network is crucial due to its broad range of applications such as transportation, map updating, navigation, and generating maps. Therefore our paper concentrates on understanding the cognitive processes, reasoning, and knowledge used by the analyst through visual cognition while performing the task of road detection from HRSi. The novel process is performed emulating human cognition within cognitive task analysis which is carried out in five different stages. The suggested cognitive procedure for road extraction is validated with the fifteen HRSi of four different land cover patterns specifically developed-sub-urban (DSUr), developed-urban (DUr), emerging-sub-urban (ESUr), and emerging-urban (EUr). The experimental results and the comparative assessment prove the impact of the presented cognitive method. Numéro de notice : A2022-506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1810330 Date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1810330 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101027
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2365 - 2384[article]Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)
[article]
Titre : Multi-modal temporal attention models for crop mapping from satellite time series Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 294 - 305 Note générale : bibliographie
This work was partly supported by ASP, the French Payment Agency.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] bande C
[Termes IGN] carte agricole
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] parcelle agricole
[Termes IGN] Pastis
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune to cloud cover. Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities. We implement and evaluate multiple fusion schemes, including a novel approach and simple adjustments to the training procedure, significantly improving performance and efficiency with little added complexity. We show that most fusion schemes have advantages and drawbacks, making them relevant for specific settings. We then evaluate the benefit of multimodality across several tasks: parcel classification, pixel-based segmentation, and panoptic parcel segmentation. We show that by leveraging both optical and radar time series, multimodal temporal attention-based models can outmatch single-modality models in terms of performance and resilience to cloud cover. To conduct these experiments, we augment the PASTIS dataset (Garnot and Landrieu, 2021a) with spatially aligned radar image time series. The resulting dataset, PASTIS-R, constitutes the first large-scale, multimodal, and open-access satellite time series dataset with semantic and instance annotations. (Dataset available at: https://zenodo.org/record/5735646) Numéro de notice : A2022-157 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.012 Date de publication en ligne : 24/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100365
in ISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 294 - 305[article]Voir aussiRéservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022051 SL Revue Centre de documentation Revues en salle Disponible 081-2022053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
[article]
Titre : Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection Type de document : Article/Communication Auteurs : David Alejandro Jimenez-Sierra, Auteur ; David Alfredo Quintero-Olaya, Auteur ; Juan Carlos Alvear-Muñoz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4410416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] graphe
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] Kappa de Cohen
[Termes IGN] lissage de données
[Termes IGN] processus gaussien
[Termes IGN] réseau sémantique
[Termes IGN] segmentation d'image
[Termes IGN] seuillage
[Termes IGN] superpixelRésumé : (auteur) Graph-based methods are promising approaches for traditional and modern techniques in change detection (CD) applications. Nonetheless, some graph-based approaches omit the existence of useful priors that account for the structure of a scene, and the inter- and intra-relationships between the pixels are analyzed. To address this issue, in this article, we propose a framework for CD based on graph fusion and driven by graph signal smoothness representation. In addition to modifying the graph learning stage, in the proposed model, we apply a Gaussian mixture model for superpixel segmentation (GMMSP) as a downsampling module to reduce the computational cost required to learn the graph of the entire images. We carry out tests on 14 real cases of natural disasters, farming, and construction. The dataset contains homogeneous cases with multispectral (MS) and synthetic aperture radar (SAR) images, along with heterogeneous cases that include MS/SAR images. We compare our approach against probabilistic thresholding, unsupervised learning, deep learning, and graph-based methods. In terms of Cohen’s kappa coefficient, our proposed model based on graph signal smoothness representation outperformed state-of-the-art approaches in ten out of 14 datasets. Numéro de notice : A2022-379 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3168126 Date de publication en ligne : 18/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3168126 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100643
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 4410416[article]Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)
[article]
Titre : Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data Type de document : Article/Communication Auteurs : Yanyan Wang, Auteur ; Shenghui Fang, Auteur ; Lingli Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte de la végétation
[Termes IGN] Chine
[Termes IGN] croissance végétale
[Termes IGN] données spatiotemporelles
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maïs (céréale)
[Termes IGN] mesure de similitude
[Termes IGN] phénologie
[Termes IGN] saison
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This study aims to map the planting area of summer maize and estimate the spatiotemporal phenology information with parcel-based classification method through integration of Sentinel-1/2 data in Jiaozuo located in North China Plain. For the maize mapping, the combination of Sentinel-1/2 data with the parcel-based method has the highest classification accuracy, suggesting that the integration of Sentinel-1/2 data with parcel-based method has great potential for regional maize mapping. For the estimation of maize phenology, the dynamic threshold method is used to extract the tasseling and milk ripening date through the time series σ0VH. In order to reduce the influence of precipitation or irrigation on SAR data, a Local Minimum Value Composite (LMVC) method is proposed to filter the original time series SAR data. The systematic phenology estimation method mainly includes LMVC, S-G filtering, Fourier curve fitting and dynamic threshold points extracting. Compared with the actual phenology date by field investigation, the errors of estimated tasseling and milk ripening date are 4.3 days and 5.5 days respectively, indicating that the time series σ0VH derived from the SAR data has great potential in spatiotemporal phenology estimation of field maize. Finally, the scattering mechanism of the maize field to C-band microwave in different growth periods was analyzed. It was also found that the phenology of maize was delayed in the coal mining subsidence areas and the areas with insufficient field management. Numéro de notice : A2022-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102720 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100121
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102720[article]Évaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine / Maxime Mérizette in XYZ, n° 170 (mars 2022)PermalinkExtraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)PermalinkMulti-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkDetection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)PermalinkSynergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkAnalysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)PermalinkBuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation / Minh On Vu Ngoc (2022)PermalinkDeep learning based 2D and 3D object detection and tracking on monocular video in the context of autonomous vehicles / Zhujun Xu (2022)PermalinkÉléments pour l'analyse et le traitement d'images : application à l'estimation de la qualité du bois / Rémy Decelle (2022)PermalinkExploring data fusion for multi-object detection for intelligent transportation systems using deep learning / Amira Mimouna (2022)PermalinkMapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkMLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images / Majedaldein Almahasneh in Machine Vision and Applications, vol 33 n° 1 (January 2022)PermalinkMonitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)PermalinkMonitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies / Guangqin Song in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkPermalinkNovel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation / Hang Zhang in Pattern recognition, vol 121 (January 2022)PermalinkA PCA-PD fusion method for change detection in remote sensing multi temporal images / Soltana Achour in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkUrban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images / Xiao Li in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)Permalink