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Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping / Jwan Al-Doski in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 8 (August 2022)
[article]
Titre : Incorporation of digital elevation model, normalized difference vegetation index, and Landsat-8 data for land use land cover mapping Type de document : Article/Communication Auteurs : Jwan Al-Doski, Auteur ; Faez M. Hassan, Auteur ; Hussein Abdelwahab Mossa, Auteur ; Aus A. Najim, Auteur Année de publication : 2022 Article en page(s) : pp 507 - 516 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données auxiliaires
[Termes IGN] image Landsat-8
[Termes IGN] Malaisie
[Termes IGN] MNS ASTER
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] ombre
[Termes IGN] précision de la classificationRésumé : (Auteur) Ancillary data are crucial in land use land cover (LULC) mapping process. This study goal is to investigate if adding Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM) data as ancillary data to the Landsat-8 spectral imagery (acquired on 14 April 2016) in the support vector machine (SVM ) classification process improves LULC mapping accuracy in GuaMusang, Malaysia. ENVI software was used to preprocess a single Landsat-8 image, convert it to reflectance, and calculate NDVI. ASTER-GDEM data were used to generate the DEM. The logical channel method was used to combine NDVI and DEM with Landsat-8 bands and limit the impact of shadows during SVM classification. The SVM accuracy was tested and evaluated on ancillary data and Landsat-8 spectral-based collection. The results revealed that the user's accuracy and producer's accuracy improved by 15.1% and 2.1%, for primary forest and by 17.93% and 28.86% for secondary forest, respectively. The classification reliability of the majority of LULC categories has increased significantly. Compared to SVM spectral-based set, the overall accuracy and kappa coefficient of the SVM ancillary-based set improved by 8.77% and 0.12, respectively. In conclusion, this article demonstrated that integrating DEM and NDVI data improves Landsat-8 image classification precision. Numéro de notice : A2022-805 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00082R2 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.14358/PERS.21-00082R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102132
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 8 (August 2022) . - pp 507 - 516[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022081 SL Revue Centre de documentation Revues en salle Disponible Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis / Daniele Cirillo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)
[article]
Titre : Integrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis Type de document : Article/Communication Auteurs : Daniele Cirillo, Auteur ; Francesca Cerritelli, Auteur ; Silvano Agostini, Auteur Année de publication : 2022 Article en page(s) : n° 437 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Apennins
[Termes IGN] carte géologique
[Termes IGN] déformation de la croute terrestre
[Termes IGN] géologie
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] post-traitement
[Termes IGN] structure-from-motionRésumé : (auteur) We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area’s tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application. Numéro de notice : A2022-648 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080437 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.3390/ijgi11080437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101464
in ISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 437[article]A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery / Sajid Ghuffar in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
Titre : A pipeline for automated processing of Corona KH-4 (1962-1972) stereo imagery Type de document : Article/Communication Auteurs : Sajid Ghuffar, Auteur ; Tobias Bolch, Auteur ; Ewelina Rupnik , Auteur ; Atanu Bhattacharya, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie
voir aussi https://research-repository.st-andrews.ac.uk/bitstream/10023/26124/1/Ghuffar_2022_IEEE_TGRS_Pipeline_automated_processing_AAM.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compensation par faisceaux
[Termes IGN] géométrie de l'image
[Termes IGN] géométrie épipolaire
[Termes IGN] glacier
[Termes IGN] Himalaya
[Termes IGN] image Corona
[Termes IGN] image panoramique
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stéréoscopique
[Termes IGN] point d'appuiRésumé : (auteur) The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time dependent exterior orientation parameters is employed. The results show that using the entire frame of the Corona image, bundle adjustment using well-distributed GCPs results in an average standard deviation (SD) of less than 2 pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the accuracy. We further assess a polynomial epipolar resampling method for rectification of Corona stereo images. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long term storage as likely cause of systematic deviations. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation (NMAD) of elevation differences of ? 4m over an area of approx. 4000km2. We show that the proposed pipeline can be applied to sequence of complex scenes involving high relief and glacierized terrain and that the resulting DEMs can be used to compute long term glacier elevation changes over large areas. Numéro de notice : A2022-952 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3200151 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3200151 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103286
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - pp[article]Transfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Transfer learning from citizen science photographs enables plant species identification in UAV imagery Type de document : Article/Communication Auteurs : Salim Soltani, Auteur ; Hannes Feilhauer, Auteur ; Robbert Duker, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données naturalistes
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] espèce végétale
[Termes IGN] filtrage de la végétation
[Termes IGN] identification de plantes
[Termes IGN] image captée par drone
[Termes IGN] orthoimage couleur
[Termes IGN] science citoyenne
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications. Numéro de notice : A2022-488 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100016 Date de publication en ligne : 23/05/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100956
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100016[article]UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)
[article]
Titre : UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment Type de document : Article/Communication Auteurs : Katerina Trepekli, Auteur ; Thomas Balstrøm, Auteur ; Thomas Friborg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 423 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] antenne GNSS
[Termes IGN] centrale inertielle
[Termes IGN] faisceau laser
[Termes IGN] Ghana
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] risque naturel
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular Network
[Termes IGN] zone urbaineRésumé : (auteur) In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings. Numéro de notice : A2022-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s11069-022-05308-9 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05308-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101567
in Natural Hazards > vol 113 n° 1 (August 2022) . - pp 423 - 451[article]Uncertainty interval estimates for computing slope and aspect from a gridded digital elevation model / Carlos López-Vázquez in International journal of geographical information science IJGIS, vol 36 n° 8 (August 2022)PermalinkAdaptive transfer of color from images to maps and visualizations / Mingguang Wu in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)PermalinkAdvancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)PermalinkExploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation / Huan Ning in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkInvestigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)PermalinkLidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkEncoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)Permalink3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkAnalysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkPhysical modelling of Nanda Devi National Park, a natural world heritage site, from GIS data / Sanat Agrawal in Cartographica, vol 57 n° 2 (Summer 2022)Permalink