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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
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Titre : Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models Type de document : Article/Communication Auteurs : Xikun Hu, Auteur ; Puzhao Zhang, Auteur ; Yifang Ban, Auteur Année de publication : 2023 Article en page(s) : pp 228 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dommage
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] jeu de données localisées
[Termes IGN] segmentation sémantique
[Termes IGN] surveillance forestière
[Termes IGN] zone sinistréeRésumé : (auteur) Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. Numéro de notice : A2023-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.026 Date de publication en ligne : 11/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102498
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 228 - 240[article]PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
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Titre : PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes Type de document : Article/Communication Auteurs : Weixiao Gao, Auteur ; Liangliang Nan, Auteur ; Bas Boom, Auteur ; Hugo Ledoux, Auteur Année de publication : 2023 Article en page(s) : pp 32 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de scène 3D
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] contour
[Termes IGN] maillage
[Termes IGN] Perceptron multicouche
[Termes IGN] réseau neuronal de graphes
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic classification. The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes. In the subsequent classification step, we construct a graph that encodes the geometric and photometric features of the segments in its nodes and the multi-scale contextual features in its edges. The final semantic segmentation is obtained by classifying the segments using a graph convolutional network. Experiments and comparisons on two semantic urban mesh benchmarks demonstrate that our approach outperforms the state-of-the-art methods in terms of boundary quality, mean IoU (intersection over union), and generalization ability. We also introduce several new metrics for evaluating mesh over-segmentation methods dedicated to semantic segmentation, and our proposed over-segmentation approach outperforms state-of-the-art methods on all metrics. Our source code is available at https://github.com/WeixiaoGao/PSSNet. Numéro de notice : A2023-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.020 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102399
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 32 - 44[article]
Titre : AI4GEO: LOD0 Generation for 3D building models Type de document : Article/Communication Auteurs : Pierre Lassalle, Auteur ; Bruno Vallet , Auteur ; Etienne Le Bihan, Auteur ; Pierre-Marie Brunet, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2023 Projets : AI4GEO / Conférence : JURSE 2023, Joint Urban Remote Sensing Event 17/05/2023 19/05/2023 Heraklion Grèce Proceedings IEEE Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] image satellite
[Termes IGN] niveau de détail
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] zone urbaineRésumé : (Auteur) Recent studies on Earth observation are improved by the proliferation of imaging sensors able to capture large datasets with a high spatial resolution. As a result, many approaches have been proposed for 3D modeling, remote sensing (RS), image processing and mapping. In this scope, three-dimensional (3D) mapping of urban areas has a great potential to provide the user with a precise scene understanding. The AI4GEO project aims at developing an automatic solution for producing 3D geospatial information with new added-value services. This paper will first introduce the AI4GEO initiative, context and overall objectives. It will then present the current status regarding 3D reconstruction of urban areas, in particular LOD0 building shape generation using satellite data. Numéro de notice : C2023-010 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE57346.2023.10144155 Date de publication en ligne : 08/06/2023 En ligne : https://doi.org/10.1109/JURSE57346.2023.10144155 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103311
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Titre : L’altimétrie radar remonte les fleuves Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2023 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Termes IGN] altimétrie satellitaire par radar
[Termes IGN] bande K
[Termes IGN] hauteurs de mer
[Termes IGN] image à haute résolution
[Termes IGN] image SWOT
[Termes IGN] niveau de l'eau
[Vedettes matières IGN] AltimétrieRésumé : (Auteur) Le niveau des océans est mesuré finement depuis trente ans. Lancé le 15 décembre dernier, le satellite franco-américain Swot offre une résolution sans précédent qui permettra de connaître le niveau des eaux continentales, y compris sur des lacs et rivières de petite taille. Numéro de notice : A2023-062 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102361
in Géomètre > n° 2209 (janvier 2023) . - pp 17 - 17[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 063-2023011 RAB Revue Centre de documentation En réserve L003 Disponible Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)
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Titre : Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data Type de document : Article/Communication Auteurs : Thuong V. Tran, Auteur ; David Bruce, Auteur ; Cho-Ying Huang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2163070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spectrale
[Termes IGN] changement d'occupation du sol
[Termes IGN] image Terra-MODIS
[Termes IGN] indice d'humidité
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] parcelle agricole
[Termes IGN] sécheresse
[Termes IGN] série temporelle
[Termes IGN] surveillance agricole
[Termes IGN] variation temporelle
[Termes IGN] Viet NamRésumé : (auteur) Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p Numéro de notice : A2023-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2022.2163070 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/15481603.2022.2163070 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102329
in GIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2163070[article]Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])
PermalinkPermalinkDetection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)
PermalinkEstimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])
PermalinkExploring the addition of airborne Lidar-DEM and derived TPI for urban land cover and land use classification and mapping / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
PermalinkGeneration of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
PermalinkA geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)
PermalinkGeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)
PermalinkGeospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)
PermalinkA hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)
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