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Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])
[article]
Titre : Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece Type de document : Article/Communication Auteurs : D.D. Alexakis, Auteur ; E.G. Stavroulaki, Auteur ; I.K. Tsanis, Auteur Année de publication : 2019 Article en page(s) : pp 703 - 721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] Crète (île)
[Termes IGN] données polarimétriques
[Termes IGN] image Landsat-8
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] lac
[Termes IGN] niveau de l'eau
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology has been successfully employed to detect water level changes and produce corresponding water level variation maps. In this study, Agia and Kournas lakes, located in Western Crete, Greece, were used as pilot areas to monitor water level change with means of SAR interferometry and auxiliary Earth Observation (EO) data. The water level variation was monitored for the period 2015–2016, using Sentinel-1A imageries and corresponding stage water level data. Landsat 8 data were additionally used to study vegetation regime and surface water extent and how these parameters affect interferograms performance. The results highlighted the fact that the combination of SAR backscattering intensity and unwrapped phase can provide additional insight into hydrological studies. The overall analysis of both interferometric characteristics and backscattering mechanism denoted their potential in enhancing the reliability of the water-level retrieval scheme and optimizing the capture of hydrological patterns spatial distribution. Numéro de notice : A2019-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1434685 Date de publication en ligne : 11/02/2018 En ligne : https://doi.org/10.1080/10106049.2018.1434685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93821
in Geocarto international > vol 34 n° 7 [01/06/2019] . - pp 703 - 721[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019071 RAB Livre Centre de documentation En réserve L003 Disponible Digital surface model generation from high resolution multi-view stereo satellite imagery / Ke Gong in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 5 (May 2019)
[article]
Titre : Digital surface model generation from high resolution multi-view stereo satellite imagery Type de document : Article/Communication Auteurs : Ke Gong, Auteur ; Dieter Fritsch, Auteur Année de publication : 2019 Article en page(s) : pp 379 - 387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] angle de visée
[Termes IGN] Argentine
[Termes IGN] chaîne de traitement
[Termes IGN] géométrie épipolaire
[Termes IGN] image multitemporelle
[Termes IGN] image Worldview
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de pointsRésumé : (Auteur) Along with improvements to spatial resolution, multiple-view stereo satellite imagery has become a valuable datasource for digital surface model generation. In 2016, a public multi-view stereo benchmark of commercial satellite imag- ery was released by the John Hopkins University Applied Physics Laboratory, USA. Motivated by this well-organized benchmark, we propose a pipeline to process multi-view satellite imagery into digital surface models. Input images are selected based on view angles and capture dates. We apply the relative bias-compensated model for orientation, and then generate the epipolar image pairs. The images are matched by the modified tube-based SemiGlobal Matching method (tSGM). Within the triangulation step, very dense point clouds are produced, and are fused by a median filter to generate the Digital Surface Model (DSM). A comparison with the reference data shows that the fused DSM generated by our pipeline is accurate and robust. Numéro de notice : A2019-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.5.379 Date de publication en ligne : 01/05/2019 En ligne : https://doi.org/10.14358/PERS.85.5.379 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92771
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 5 (May 2019) . - pp 379 - 387[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019051 SL Revue Centre de documentation Revues en salle Disponible Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)
[article]
Titre : Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) Type de document : Article/Communication Auteurs : Wenzhi Zhao, Auteur ; Yanchen Bo, Auteur ; Jiage Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 237 - 250 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classe sémantique
[Termes IGN] compréhension de l'image
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] reconnaissance d'objets
[Termes IGN] scène urbaineRésumé : (Auteur) Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images. Numéro de notice : A2019-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.019 Date de publication en ligne : 29/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92675
in ISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 237 - 250[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Fusion of thermal imagery with point clouds for building façade thermal attribute mapping / Dong Lin in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)
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Titre : Fusion of thermal imagery with point clouds for building façade thermal attribute mapping Type de document : Article/Communication Auteurs : Dong Lin, Auteur ; Malgorzata Jarząbek-Rychard, Auteur ; Xiaochong Tong, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2019 Article en page(s) : pp 162 - 175 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] façade
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] texturageRésumé : (Auteur) Thermal image data are widely used to assess the insulation quality of buildings and to detect thermal leakages. In our approach, we merge terrestrial thermal image data and 3D point clouds to perform thermal texture mapping for building facades. Since geo-referencing data of a hand-held thermal camera is usually not available in such applications, registration between thermal images and a 3D point cloud (for instance generated from RGB image data by structure-from-motion techniques) is essential. In our approach, thermal image data registration is conducted in four steps: First, another point cloud is generated from the thermal image data. Next, a coarse registration between thermal point cloud and RGB point cloud is performed using the fast global registration (FGR) algorithm. The best corresponding thermal-RGB image pairs are acquired by picking up the lowest Euclidean distance between the exterior orientation parameters of thermal images and transformed exterior orientation parameters of RGB images. Subsequently, radiation-invariant feature transform (RIFT), normalized barycentric coordinate system (NBCS) and random sample consensus (RANSAC) are employed to extract reliable matching features on thermal-RGB image pairs. Afterwards, a fine registration is performed by mono-plotting of the RGB image, followed by image resection of the thermal image. Finally, in terms of texture mapping algorithms, in order to remove the blur effects caused by small misalignments for different candidate images, a global image pose refinement approach, which aims to minimize the temperature disagreements provided by different images for the same object points, is proposed. In addition, in order to ensure high geometric and radiant accuracy, camera calibrations are performed. Experiments showed that the proposed method could not only achieve high geometric registration accuracy, but also provide a good radiometric accuracy with RMSE lower than 1.5 °C. Numéro de notice : A2019-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.010 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92674
in ISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 162 - 175[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Multi-temporal image change mining based on evidential conflict reasoning / Fatma Haouas in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)
[article]
Titre : Multi-temporal image change mining based on evidential conflict reasoning Type de document : Article/Communication Auteurs : Fatma Haouas, Auteur ; Basel Solaiman, Auteur ; Zouhour Ben Dhiaf, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 59 - 75 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] conflit d'intégration
[Termes IGN] détection de changement
[Termes IGN] image Landsat
[Termes IGN] image multitemporelle
[Termes IGN] raisonnement spatial
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] visibilité spatio-temporelleRésumé : (Auteur) Change detection monitoring on multi-temporal remote sensed images is a persistent methodological challenge where the Dempster-Shafer, or evidence, Theory (DST) has been often applied. This paper presents a new method based on the use of DST for mining bi-temporal remotely sensed images change. The main idea is based on the investigation, analysis and interpretation of different types of conflict between two bi-temporal mass distributions. The reasoning process is focused on the conflict significance and its “partial” causes. In fact, the global conflict that occurs during the joint exploitation of multi-temporal images gives general and non-sufficiently concise information. However, the partial conflict provides rich and important information with regards to the disagreement between knowledge sources. For computing the partial conflict between focal elements, the geometric representation of mass distributions is exploited. The obtained conflict measures, caused by change, are analyzed latter by a new algorithm for drifting binary change map and identifying change directions. The effectiveness and reliability of the proposed approach are shown through experimentations on simulated changed images as well as using multi-temporal Landsat satellite images where qualitative criteria as well as quantitative measures are applied. The performances of the proposed approach, in terms of changed area recognition, are compared to three different and widely used conflict measures: the Empty-set mass, the Jousselme’s distance and the Cosine measure. It is shown that the developed change detection approach outperforms these conflict measures. Numéro de notice : A2019-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.018 Date de publication en ligne : 13/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92667
in ISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 59 - 75[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkRetrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth / Sébastien Labarre in Remote sensing of environment, vol 225 (May 2019)PermalinkVirtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)Permalink3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkConditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkHyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkLearning to segment moving objects / Pavel Tokmakov in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkA novel sharpening approach for superresolving multiresolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkSemantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkTree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkComplete 3D scene parsing from an RGBD image / Chuhang Zou in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkEfficiently annotating object images with absolute size information using mobile devices / Martin Hofmann in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkSmart cartographic background symbolization for map mashups in geoportals : A proof of concept by example of landuse representation / Nadia H. 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