Descripteur
Documents disponibles dans cette catégorie (8656)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
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]PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)
[article]
Titre : PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Scott Hensley, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 123 - 139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] bande L
[Termes IGN] données lidar
[Termes IGN] forêt boréale
[Termes IGN] forêt tropicale
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur de la végétation
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] polarimétrie radar
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réseau antagoniste génératif
[Termes IGN] semis de pointsRésumé : (auteur) This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). Numéro de notice : A2022-195 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.02.008 Date de publication en ligne : 17/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99962
in ISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 123 - 139[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022041 SL Revue Centre de documentation Revues en salle Disponible 081-2022043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)
[article]
Titre : Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Cunjian Yang, Auteur ; Zengyang Yu, Auteur Année de publication : 2022 Article en page(s) : pp 223 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cognition
[Termes IGN] détection d'objet
[Termes IGN] extraction automatique
[Termes IGN] géolocalisation
[Termes IGN] image à haute résolution
[Termes IGN] intelligence artificielle
[Termes IGN] reconnaissance automatique
[Termes IGN] zone urbaineRésumé : (auteur) Machine intelligent perception (MIP) provides a novel way for human beings to recognize geographical locations automatically. MIP of geographical locations enables computers to describe locations automatically and quantitatively by extracting Earth's surface features and building relationships. The earth surface fingerprint is established here by mining the relationship between spatial objects with stable characteristics extracted from urban high-resolution remote sensing images, which realizes intelligent perception of geographical location innovatively. Mask Region-based Convolutional Neural Network is used to automatically extract the spatial objects such as playgrounds, crossroads, and bridges from the images. Then, the extracted spatial objects are encoded according to the landuse type, distance, and angle of 24 nearest objects to construct urban surface fingerprint database. The urban surface fingerprint database is used to match the geographical location of spatial objects in local images so that the matching algorithm can be used for machine recognition of the geographical location of specific objects in the target image. Taking the main cities in China as the experimental area, the success rate of location perception is 92%. We have made a useful exploration in the field of MIP of geographical location, hoping to promote the development of human cognition of geographical location. Numéro de notice : A2022-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00017R3 Date de publication en ligne : 04/04/2022 En ligne : https://doi.org/10.14358/PERS.21-00017R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100319
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 4 (April 2022) . - pp 223 - 231[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022041 SL Revue Centre de documentation Revues en salle Disponible Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data / Zihao Huang in Remote sensing, vol 14 n° 7 (April-1 2022)
[article]
Titre : Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data Type de document : Article/Communication Auteurs : Zihao Huang, Auteur ; Xuejian Li, Auteur ; Qiang Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1698 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] automate cellulaire
[Termes IGN] changement climatique
[Termes IGN] changement d'utilisation du sol
[Termes IGN] Chine
[Termes IGN] écosystème forestier
[Termes IGN] forêt tropicale
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] interaction homme-milieu
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] modèle numérique de surface
[Termes IGN] puits de carbone
[Termes IGN] simulation spatialeRésumé : (auteur) Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province. Numéro de notice : A2022-281 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14071698 Date de publication en ligne : 31/03/2022 En ligne : https://doi.org/10.3390/rs14071698 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100297
in Remote sensing > vol 14 n° 7 (April-1 2022) . - n° 1698[article]Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])
[article]
Titre : Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery Type de document : Article/Communication Auteurs : Semiha Demirbaş Çağlayana, Auteur ; Ugur Murat Leloglu, Auteur ; Christian Ginzler, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1587 - 1606 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Arbutus unedo
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données multitemporelles
[Termes IGN] Erica (genre)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt méditerranéenne
[Termes IGN] Genista (genre)
[Termes IGN] gestion forestière durable
[Termes IGN] image Sentinel-MSI
[Termes IGN] maquis
[Termes IGN] Olea europaea
[Termes IGN] TurquieRésumé : (auteur) Essential forest ecosystem services can be assessed by better understanding the diversity of vegetation, specifically those of Mediterranean region. A species level classification of maquis would be useful in understanding vegetation structure and dynamics, which would be an indicator of degradation or succession in the region. Although remote sensing was regularly used for classification in the region, maquis are simply represented as one to three categories based on density or height. To fill this gap, we test the capability of Sentinel-2 imagery, together with selected ancillary variables, for an accurate mapping of the dominant maquis formations. We applied Recursive Feature Selection procedure and used a Random Forest classifier. The algorithm is tested using ground truth collected from site and reached 78% and 93% overall accuracy at species level and physiognomic level, respectively. Our results suggest species level characterization of dominant maquis is possible with Sentinel-2 spatial resolution. Numéro de notice : A2022-475 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1783581 Date de publication en ligne : 09/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1783581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100822
in Geocarto international > vol 37 n° 6 [01/04/2022] . - pp 1587 - 1606[article]The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)PermalinkUncertainty estimation for stereo matching based on evidential deep learning / Chen Wang in Pattern recognition, vol 124 (April 2022)PermalinkUrban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)PermalinkTwo-phase forest inventory using very-high-resolution laser scanning / Henrik J. Persson in Remote sensing of environment, vol 271 (March- 2 2022)PermalinkA l'aide ! Je me suis perdu en zoomant / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkAn approach to extracting digital elevation model for undulating and hilly terrain using de-noised stereo images of Cartosat-1 sensor / Litesh Bopche in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkAutomated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkAutomatic extraction of building geometries based on centroid clustering and contour analysis on oblique images taken by unmanned aerial vehicles / Leilei Zhang in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)PermalinkChallenges related to the determination of altitudes of mountain peaks presented on cartographic sources / Katarzyna Chwedczuk in Geodetski vestnik, vol 66 n° 1 (March 2022)PermalinkComparaison 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)Permalink