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Auteur Michael Ying Yang |
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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 Our motivation to study 3D change detection is mainly related to the practical need to update the outdated point clouds captured by Airborne Laser Scanning (ALS) with new point clouds obtained by dense image matching (DIM).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 vol V-4-2021 - July 2021 - [actes] XXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission 4, 2021 edition, 5–9 July 2021 (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Nicolas Paparoditis
Preface: the 2021 edition of the XXIVth ISPRS congress / Clément Mallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)
Titre : Preface: the 2021 edition of the XXIVth ISPRS congress Type de document : Article/Communication Auteurs : Clément Mallet Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 1, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 1 Article en page(s) : pp 1 - 5 Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) We report key elements and figures related to the proceedings of the 2021 edition of the XXIVth ISPRS Congress. Similarly to 2020, the COVID-19 pandemic caused global travel challenges and restrictions for the first half of 2021. Consequently, the physical Congress re-scheduled from June 2020 to July 2021 was again postponed to June 2022, still in Nice (France). Papers were already submitted and the ISPRS Council decided to carry out the review process and the publication of the proceedings of the papers submitted under the label “2021 Edition”. The authors of published papers had the opportunity to present their work during a Digital Event, this year scheduled the same week as the planned Congress (5–9 July 2021). Numéro de notice : A2021-613 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-1-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-4-2021-1-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97948
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2021 (July 2021) . - pp 1 - 5[article]
Titre : Dynamic scene understanding using deep neural networks Type de document : Thèse/HDR Auteurs : Ye Lyu, Auteur ; M. George Vosselman, Directeur de thèse ; Michael Ying Yang, Directeur de thèse Editeur : Enschede [Pays-Bas] : International Institute for Geo-Information Science and Earth Observation ITC Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] chaîne de traitement
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] image captée par drone
[Termes IGN] image vidéo
[Termes IGN] poursuite de cible
[Termes IGN] régression
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Scene understanding is an important and fundamental research field in computer vision, which is quite useful for many applications in photogrammetry and remote sensing. It focuses on locating and classifying objects in images, understanding the relationships between them. The higher goal is to interpret what event happens in the scene, when it happens and why it happens, and what should we do based on the information. Dynamic scene understanding is to use information from different time to interpret scenes and answer the above related questions. For modern scene understanding technology, deep learning has shown great potential for such task. "Deep" in deep learning refers to the use of multiple layers in the neural networks. Deep neural networks are powerful as they are highly non-linear function that possess the ability to map from one domain to another quite different domain after proper training. It is the best solution for many fundamental research tasks regarding scene understanding. This ph.D. research also takes advantage of deep learning for dynamic scene understanding. Temporal information plays an important role for dynamic scene understanding. Compared with static scene understanding from images, information distilled from the time dimension provides values in many different ways. Images across consecutive frames have very high correlation, i.e., objects observed in one frame have very high chance to be observed and identified in nearby frames as well. Such redundancy in observation could potentially reduce the uncertainty for object recognition with deep learning based methods, resulting in more consistent inference. High correlation across frames could also improve the chance for recognizing objects correctly. If the camera or the object moves, the object could be observed in multiple different views with different poses and appearance. The information captured for object recognition would be more diverse and complementary, which could be aggregated to jointly inference the categories and the properties of objects. This ph.D. research involves several tasks related to the dynamic scene understanding in computer vision, including semantic segmentation for aerial platform images (chapter 2, 3), video object segmentation and video object detection for common objects in natural scenes (chapter 4, 5), and multi-object tracking and segmentation for cars and pedestrians in driving scenes (chapter 6). Chapter2 investigates how to establish the semantic segmentation benchmark for the UAV images, which includes data collection, data labeling, dataset construction, and performance evaluation with baseline deep neural networks and the proposed multi-scale dilation net. Conditional random field with feature space optimization is used to achieve consistent semantic segmentation prediction in videos. Chapter3 investigates how to better extract the scene context information for etter object recognition performance by proposing the novel bidirectional multiscale attention networks. It achieves better performance by inferring features and attention weights for feature fusing from both higher level and lower level branches. Chapter4 investigates how to simultaneously segment multiple objects across multiple frames by combining memory modules with instance segmentation networks. Our method learns to propagate the target object labels without auxiliary data, such as optical flow, which simplifies the model. Chapter5 investigates how to improve the performance of well-trained object detectors with a light weighted and efficient plug&play tracker for object detection in video. This chapter also investigates how the proposed model performs when lacking video training data. Chapter6 investigates how to improve the performance of detection, segmentation, and tracking by jointly considering top-down and bottom-up inference. The whole pipeline follows the multi-task design, i.e., a single feature extraction backbone with multiple heads for different sub-tasks. Overall, this manuscript has delved into several different computer vision tasks, which share fundamental research problems, including detection, segmentation, and tracking. Based on the research experiments and knowledge from literature review, several reflections regarding dynamic scene understanding have been discussed: The range of object context influence the quality for object recognition; The quality of video data affect the method choice for specific computer vision task; Detection and tracking are complementary for each other. For future work, unified dynamic scene understanding task could be a trend, and transformer plus self-supervised learning is one promising research direction. Real-time processing for dynamic scene understanding requires further researches in order to put the methods into usage for real-world applications. Numéro de notice : 12984 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 : 2021 DOI : 10.3990/1.9789036552233 Date de publication en ligne : 08/09/2021 En ligne : https://library.itc.utwente.nl/papers_2021/phd/lyu.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100962
Titre : Vehicle detection in aerial images Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Xinbo Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 297 - 304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] entropie
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] précision de la classification
[Termes IGN] qualité d'image
[Termes IGN] réseau neuronal convolutif
[Termes IGN] véhicule automobileRésumé : (Auteur) The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection. Numéro de notice : A2019-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.4.297 Date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.14358/PERS.85.4.297 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92568
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 4 (avril 2019) . - pp 297 - 304[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019041 SL Revue Centre de documentation Revues en salle Disponible
