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Auteur Markus Gerke |
Documents disponibles écrits par cet auteur (12)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesPhotogrammetric point clouds: quality assessment, filtering, and change detection / Zhenchao Zhang (2022)
Titre : Photogrammetric point clouds: quality assessment, filtering, and change detection Type de document : Thèse/HDR Auteurs : Zhenchao Zhang, Auteur ; M. George Vosselman, Auteur ; Markus Gerke, Auteur ; Michael Ying Yang, Auteur Editeur : Enschede [Pays-Bas] : International Institute for Geo-Information Science and Earth Observation ITC Année de publication : 2022 Note générale : bibliographie
NB : EMBARGO SUR LE TEXTE JUSQU'AU 1ER JUILLET 2022Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] qualité des données
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) 3D change detection draws more and more attention in recent years due to the increasing availability of 3D data. It can be used in the fields of land use / land cover (LULC) change detection, 3D geographic information updating, terrain deformation analysis, urban construction monitoring et al. Our motivation to study 3D change detection is mainly related to the practical need to update the outdated point clouds captured by Airborne Laser Scanning (ALS) with new point clouds obtained by dense image matching (DIM).
The thesis has three main parts. The first part, chapter 1, explains the motivation, providing a review of current ALS and airborne photogrammetry techniques. It also presents the research objectives and questions. The second part including chapter 2 and chapter 3 evaluates the quality of photogrammetric products and investigates their potential for change detection. The third part including chapter 4 and chapter 5 proposes two methods for change detection that meet different requirements.
To investigate the potential of using point clouds derived by dense matching for change detection, we propose a framework for evaluating the quality of 3D point clouds and DSMs generated by dense image matching. Our evaluation framework based on a large number of square patches reveals the distribution of dense matching errors in the whole photogrammetric block. Robust quality measures are proposed to indicate the DIM accuracy and precision quantitatively. The overall mean offset to the reference is 0.1 Ground Sample Distance (GSD); the maximum mean deviation reaches 1.0 GSD. We also find that the distribution of dense matching errors is homogenous in the whole block and close to a normal distribution based on many patch-based samples. However, in some locations, especially along narrow alleys, the mean deviations may get worse. In addition, the profiles of ALS points and DIM points reveal that the DIM profile fluctuates around the ALS profile. We find that the accuracy of DIM point cloud improves and that the noise level decreases on smooth ground areas when oblique images are used in dense matching together with nadir images.
Then we evaluate whether the standard LiDAR filters are effective to filter dense matching points in order to derive accurate DTMs. Filtering results on a city block show that LiDAR filters perform well on the grassland, along bushes and around individual trees if the point cloud is sufficiently precise. When a ranking filter is used on the point clouds before filtering, the filtering will identify fewer but more reliable ground points. However, some small objects on the terrain will be filtered out. Since we aim at obtaining accurate DTMs, the ranking filter shows its value in identifying reliable ground points. Based on the previous findings in DIM quality, we propose a method to detect building changes between ALS and photogrammetric data. Firstly, the ALS points and DIM points are split out and concatenated with the orthoimages. The multimodal data are normalized to feed into a pseudo-Siamese Neural network for change detection. Then, the changed objects are delineated through per-pixel classification and artefact removal. The change detection module based on a pseudo-Siamese CNN can quickly localize the changes and generate coarse change maps. The next module can be used in precise mapping of change boundaries. Experimental results show that the proposed pseudo-Siamese Neural network can cope with the DIM errors and output plausible change detection results. Although the point cloud quality from dense matching is not as fine as laser scanning points, the spectral and textural information provided by the orthoimages serve as a supplement.
Considering that the tasks of semantic segmentation and change detection are correlated, we propose SiamPointNet++ model to combine the two tasks in one framework. The method outputs a pointwise joint label for each ALS point. If an ALS point is unchanged, it is assigned a semantic label; If an ALS point is changed, it is assigned a change label. The sematic and change information are included in the joint labels with minimum information redundancy. The combined Siamese network learns both intra-epoch and inter-epoch features. Intra-epoch features are extracted at multiple scales to embed the local and global information. Inter-epoch features are extracted by Conjugated Ball Sampling (CBS) and concatenated to make change inference. Experiments on the Rotterdam data set indicate that the network is effective in learning multi-task features. It is invariant to the permutation and noise of inputs and robust to the data difference between ALS and DIM data. Compared with a sophisticated object-based method and supervised change detection, this method requires much less hyper-parameters and human intervention but achieves superior performance.
As a conclusion, the thesis evaluates the quality of dense matching points and investigates its potential of updating outdated ALS points. The two change detection methods developed for different applications show their potential in the automation of topographic change detection and point cloud updating. Future work may focus on improving the generalizability and interpretability of the proposed models.Numéro de notice : 20403 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geo-Information Science and Earth Observation : Enschede, university of Twente : 2022 DOI : 10.3990/1.9789036552653 Date de publication en ligne : 14/01/2022 En ligne : https://research.utwente.nl/en/publications/photogrammetric-point-clouds-quality [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100963 Co‐registration of panoramic mobile mapping images and oblique aerial images / Phillipp Jende in Photogrammetric record, vol 34 n° 166 (June 2019)
Titre : Co‐registration of panoramic mobile mapping images and oblique aerial images Type de document : Article/Communication Auteurs : Phillipp Jende, Auteur ; Francesco Nex, Auteur ; M. George Vosselman, Auteur ; Markus Gerke, Auteur Année de publication : 2019 Article en page(s) : pp 148 - 173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] façade
[Termes IGN] image aérienne oblique
[Termes IGN] image panoramique
[Termes IGN] image terrestre
[Termes IGN] point d'appui
[Termes IGN] semis de points clairsemés
[Termes IGN] superposition d'imagesRésumé : (Auteur) Mobile mapping relies on satellite‐based positioning, which suffers from line‐of‐sight and multipath issues. As an alternative, this paper presents a fully automatic approach for the co‐registration of mobile mapping and oblique aerial images to introduce highly accurate and reliable ground control for mobile mapping data adjustment. An oblique view of a scene introduces similarities as well as challenges regarding co‐registration with mobile mapping images, which is supported by mutual planes in both datasets. Façade planes from a sparse point cloud are used as projection surfaces for the mobile mapping and aerial datasets, overcoming large perspective differences between them to simplify the registration. The performance of the procedure indicates an inlier rate of around 80%. Numéro de notice : A2019-245 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12276 Date de publication en ligne : 29/04/2019 En ligne : https://doi.org/10.1111/phor.12276 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92956
in Photogrammetric record > vol 34 n° 166 (June 2019) . - pp 148 - 173[article]
Titre : A fully automatic approach to register mobile mapping and airborne imagery to support the correction of plateform trajectories in GNSS-denied urban areas Type de document : Article/Communication Auteurs : Phillipp Jende, Auteur ; Francesco Nex, Auteur ; Markus Gerke, Auteur ; M. George Vosselman, Auteur Année de publication : 2018 Article en page(s) : pp 86 - 99 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement géométrique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Wallis
[Termes IGN] GNSS assisté pour la navigation
[Termes IGN] image aérienne
[Termes IGN] image terrestre
[Termes IGN] orientation d'image
[Termes IGN] orthoimage
[Termes IGN] point d'appui
[Termes IGN] précision décimétrique
[Termes IGN] système de numérisation mobile
[Termes IGN] zone urbaineRésumé : (Auteur) Mobile Mapping (MM) solutions have become a significant extension to traditional data acquisition methods over the last years. Independently from the sensor carried by a platform, may it be laser scanners or cameras, high-resolution data postings are opposing a poor absolute localisation accuracy in urban areas due to GNSS occlusions and multipath effects. Potentially inaccurate position estimations are propagated by IMUs which are furthermore prone to drift effects. Thus, reliable and accurate absolute positioning on a par with MM's high-quality data remains an open issue. Multiple and diverse approaches have shown promising potential to mitigate GNSS errors in urban areas, but cannot achieve decimetre accuracy, require manual effort, or have limitations with respect to costs and avail-ability. This paper presents a fully automatic approach to support the correction of MM imaging data based on correspondences with airborne nadir images. These correspondences can be employed to correct the MM plat-form's orientation by an adjustment solution. Unlike MM as such, aerial images do not suffer from GNSS oc-clusions, and their accuracy is usually verified by employing well-established methods using ground control points. However, a registration between MM and aerial images is a non-standard matching scenario, and requires several strategies to yield reliable and accurate correspondences. Scale, perspective and content strongly vary between both image sources, thus traditional feature matching methods may fail. To this end, the registration process is designed to focus on common and clearly distinguishable elements, such as road markings, manholes, or kerbstones. With a registration accuracy of about 98%, reliable tie information between MM and aerial data can be derived. Even though, the adjustment strategy is not covered in its entirety in this paper, accuracy results after adjustment will be presented. It will be shown that a decimetre accuracy is well achievable in a real data test scenari Numéro de notice : A2018-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.017 Date de publication en ligne : 30/04/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90397
in ISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 86 - 99[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Using UAVs for map creation and updating: A case study in Rwanda Type de document : Article/Communication Auteurs : Mila Koeva, Auteur ; M. Muneza, Auteur ; Caroline M. Gevaert, Auteur ; Markus Gerke, Auteur ; Francesco Nex, Auteur Année de publication : 2018 Article en page(s) : pp 312 - 325 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] image captée par drone
[Termes IGN] orthophotoplan numérique
[Termes IGN] précision centimétrique
[Termes IGN] Rwanda
[Termes IGN] scène urbaine
[Termes IGN] zone urbaineRésumé : (Auteur) Aerial or satellite images are conventionally used for geospatial data collection. However, unmanned aerial vehicles (UAVs) are emerging as a suitable technology for providing very high spatial and temporal resolution data at a low cost. This paper aims to show the potential of using UAVs for map creation and updating. The whole workflow is introduced in the paper, using a case study in Rwanda, where 954 images were collected with a DJI Phantom 2 Vision Plus quadcopter. An orthophoto covering 0.095 km2 with a spatial resolution of 3.3 cm was produced and used to extract features with a sub-decimetre accuracy. Quantitative and qualitative control of the UAV data products were performed, indicating that the obtained accuracies comply to international standards. Moreover, possible problems and further perspectives were also discussed. The results demonstrate that UAVs provide promising opportunities to create high-resolution and highly accurate orthophotos, thus facilitating map creation and updating. Numéro de notice : A2018-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2016.1268756 Date de publication en ligne : 30/12/2016 En ligne : https://doi.org/10.1080/00396265.2016.1268756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91014
in Survey review > vol 50 n° 361 (July 2018) . - pp 312 - 325[article]
Titre : Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images Type de document : Article/Communication Auteurs : Zhihua Xua, Auteur ; Lixin Wud, Auteur ; Markus Gerke, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 113 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] appariement de points
[Termes IGN] connexité (topologie)
[Termes IGN] drone
[Termes IGN] prise de vue aérienne
[Termes IGN] reconstruction 3D
[Termes IGN] squelettisation
[Termes IGN] structure-from-motion
[Termes IGN] topologieRésumé : (Auteur) Structure-from-Motion (SfM) techniques have been widely used for 3D scene reconstruction from multi-view images. However, due to the large computational costs of SfM methods there is a major challenge in processing highly overlapping images, e.g. images from unmanned aerial vehicles (UAV). This paper embeds a novel skeletal camera network (SCN) into SfM to enable efficient 3D scene reconstruction from a large set of UAV images. First, the flight control data are used within a weighted graph to construct a topologically connected camera network (TCN) to determine the spatial connections between UAV images. Second, the TCN is refined using a novel hierarchical degree bounded maximum spanning tree to generate a SCN, which contains a subset of edges from the TCN and ensures that each image is involved in at least a 3-view configuration. Third, the SCN is embedded into the SfM to produce a novel SCN-SfM method, which allows performing tie-point matching only for the actually connected image pairs. The proposed method was applied in three experiments with images from two fixed-wing UAVs and an octocopter UAV, respectively. In addition, the SCN-SfM method was compared to three other methods for image connectivity determination. The comparison shows a significant reduction in the number of matched images if our method is used, which leads to less computational costs. At the same time the achieved scene completeness and geometric accuracy are comparable. Numéro de notice : A2016--016 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83878
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 113 - 127[article]vol 93 - July 2014 - Urban object extraction (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Franz Rottensteiner
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