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Termes IGN > imagerie
imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
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Titre : SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images Type de document : Article/Communication Auteurs : Daifeng Peng, Auteur ; Lorenzo Bruzzone, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5891 - 5906 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches. Numéro de notice : A2021-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011913 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011913 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97986
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5891 - 5906[article]Spatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)
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Titre : Spatio-temporal-spectral observation model for urban remote sensing Type de document : Article/Communication Auteurs : Zhenfeng Shao, Auteur ; Wenfu Wu, Auteur ; Deren Li, Auteur Année de publication : 2021 Article en page(s) : pp 372 - 386 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] biomasse aérienne
[Termes IGN] cartographie des risques
[Termes IGN] complexité
[Termes IGN] fusion d'images
[Termes IGN] image satellite
[Termes IGN] inondation
[Termes IGN] modèle mathématique
[Termes IGN] scène urbaine
[Termes IGN] surface imperméable
[Termes IGN] zone urbaineMots-clés libres : spatio-temporal-spectral observation model Résumé : (auteur) Taking cities as objects being observed, urban remote sensing is an important branch of remote sensing. Given the complexity of the urban scenes, urban remote sensing observation requires data with a high temporal resolution, high spatial resolution, and high spectral resolution. To the best of our knowledge, however, no satellite owns all the above characteristics. Thus, it is necessary to coordinate data from existing remote sensing satellites to meet the needs of urban observation. In this study, we abstracted the urban remote sensing observation process and proposed an urban spatio-temporal-spectral observation model, filling the gap of no existing urban remote sensing framework. In this study, we present four applications to elaborate on the specific applications of the proposed model: 1) a spatio-temporal fusion model for synthesizing ideal data, 2) a spatio-spectral observation model for urban vegetation biomass estimation, 3) a temporal-spectral observation model for urban flood mapping, and 4) a spatio-temporal-spectral model for impervious surface extraction. We believe that the proposed model, although in a conceptual stage, can largely benefit urban observation by providing a new data fusion paradigm. Numéro de notice : A2021-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/10095020.2020.1864232 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/10095020.2020.1864232 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98642
in Geo-spatial Information Science > vol 24 n° 3 (July 2021) . - pp 372 - 386[article]Target-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
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Titre : Target-constrained interference-minimized band selection for hyperspectral target detection Type de document : Article/Communication Auteurs : Xiaodi Shang, Auteur ; Meiping Song, Auteur ; Yulei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6044 - 6064 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande spectrale
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] interférence
[Termes IGN] rapport signal sur bruitRésumé : (auteur) Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBS*) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods. Numéro de notice : A2021-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010826 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97987
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6044 - 6064[article]A unified framework of bundle adjustment and feature matching for high-resolution satellite images / Xiao Ling in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)
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Titre : A unified framework of bundle adjustment and feature matching for high-resolution satellite images Type de document : Article/Communication Auteurs : Xiao Ling, Auteur ; Xu Huang, Auteur ; Rongjun Qin, Auteur Année de publication : 2021 Article en page(s) : pp 485 - 490 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] compensation par faisceaux
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] orientation du capteur
[Termes IGN] précision radiométriqueRésumé : (Auteur) Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature matching often contains high uncertainties in weak/repeat textures, while BA results are helpful in reducing these uncertainties. To compute more accurate orientations, this article incorporates BA and feature matching in a unified framework and formulates the union as the optimization of a global energy function so that the solutions of the BA and feature matching are constrained with each other. To avoid a degeneracy in the optimization, we propose a comprised solution by breaking the optimization of the global energy function into two-step suboptimizations and compute the local minimums of each suboptimization in an incremental manner. Experiments on multi-view high-resolution satellite images show that our proposed method outperforms state-of-the-art orientation techniques with or without accurate least-squares matching. Numéro de notice : A2021-571 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.485 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.14358/PERS.87.7.485 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98163
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 7 (July 2021) . - pp 485 - 490[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021071 SL Revue Centre de documentation Revues en salle Disponible Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (Case study: Hyrcanian mixed forest) / Vahid Nasiri in Canadian Journal of Forest Research, Vol 51 n° 7 (July 2021)
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Titre : Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (Case study: Hyrcanian mixed forest) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Ali Asghar Darvishsefat, Auteur ; Hossein Arefi, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Manochehr Namiranian, Auteur ; Arnaud Le Bris
, Auteur
Année de publication : 2021 Projets : 1-Pas de projet / Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] diamètre des arbres
[Termes IGN] filtre passe-bas
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] peuplement mélangé
[Termes IGN] segmentationRésumé : (Auteur) Tree height and crown diameter are two common individual tree attributes that can be estimated from Unmanned Aerial Vehicles (UAVs) images thanks to photogrammetry and structure from motion. This research investigates the potential of low-cost UAV aerial images to estimate tree height and crown diameter. Two successful flights were carried out in two different seasons corresponding to leaf-off and leaf-on conditions to generate Digital Terrain Model (DTM) and Digital Surface Model (DSM), which were further employed in calculation of a Canopy Height Model (CHM). The CHM was used to estimate tree height using low pass and local maximum filters, and crown diameter was estimated based on an Invert Watershed Segmentation (IWS) algorithm. UAV-based tree height and crown diameter estimates were validated against field measurements and resulted in 3.22 m (10.1%) and 0.81 m (7.02%) RMSE, respectively. The results showed high agreement between our estimates and field measurements, with R2=0.808 for tree height and R2=0.923 for crown diameter. Generally, the accuracy of the results was considered acceptable and confirmed the usefulness of this approach for estimating tree heights and crown diameter. Numéro de notice : A2021-296 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1139/cjfr-2020-0125 Date de publication en ligne : 26/01/2021 En ligne : https://dx.doi.org/10.1139/cjfr-2020-0125 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97376
in Canadian Journal of Forest Research > Vol 51 n° 7 (July 2021)[article]Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data / Niels Lindgren in Scandinavian journal of forest research, vol 36 n° 5 ([01/07/2021])
PermalinkForest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkMarrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkTowards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkCoral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])
PermalinkFast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)
PermalinkAn automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)
PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
PermalinkApplication of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])
PermalinkCloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])
PermalinkComparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)
PermalinkDiscovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins / Peter T. Fretwell in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
PermalinkDomain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkForest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkFractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkA high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
PermalinkMapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
PermalinkMask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)
PermalinkModel-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkMultiscale cloud detection in remote sensing images using a dual convolutional neural network / Markku Luotamo in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkMultiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkOn the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])
PermalinkPolSAR ship detection based on neighborhood polarimetric covariance matrix / Tao Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles in XYZ, n° 167 (juin 2021)
PermalinkReference evapotranspiration (ETo) methods implemented as ArcMap models with remote-sensed and ground-based inputs, examined along with MODIS ET, for Peloponnese, Greece / Stavroula Dimitriadou in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)
PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
PermalinkRetrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkSemantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol 80 n° 15 (June 2021)
PermalinkSpatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
PermalinkThe use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries / Nagihan Aslan in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)
PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)
PermalinkAnalysing the impact of climate change on hydrological ecosystem services in Laguna del Sauce (Uruguay) using the SWAT model and remote sensing data / Celina Aznarez in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkA Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])
PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkA deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkSpherically optimized RANSAC aided by an IMU for Fisheye Image Matching / Anbang Liang in Remote sensing, vol 13 n°10 (May-2 2021)
PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)
PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)
PermalinkDetection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])
PermalinkDigital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy / Sergio Jiménez-Jiménez in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])
PermalinkEvaluation of light pollution in global protected areas from 1992 to 2018 / Haowei Mu in Remote sensing, vol 13 n° 9 (May-1 2021)
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