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Optimization of deep neural networks: A functional perspective with applications in image classification / Simon Roburin (2022)
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Titre : Optimization of deep neural networks: A functional perspective with applications in image classification Type de document : Thèse/HDR Auteurs : Simon Roburin, Auteur ; Mathieu Aubry, Directeur de thèse Editeur : Champs-sur-Marne : Ecole des Ponts ParisTech Année de publication : 2022 Importance : 141 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Ecole des Ponts ParisTech, spécialité Mathématiques AppliquéesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage profond
[Termes IGN] classification par nuées dynamiques
[Termes IGN] mathématiques appliquées
[Termes IGN] optimisation (mathématiques)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Despite numerous successes in a wide range of industrial and scientific applications, the learning process of deep neural networks is poorly understood. Loosely speaking, learning aims at finding the network parameters that not only minimize the network errors on a set of training examples but also yield correct predictions on unseen data. Under the prism of optimization, it boils down to minimizing a high dimensional non-convex function. Generalization can generally be expected when one has access to very large datasets and assumes that both training examples and unseen data are sampled from identically independently distributed random variables. The goal of this thesis is to develop analytical tools to better understand neural network optimization and to improve the design of training algorithms in the context of image classification. Note de contenu : 1- Introduction
2- Literature review
3- Impact of Normalization Layers on Optimization
4- Avoid learning spurious correlations
5- ConclusionNuméro de notice : 24098 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Ponts ParisTech : 2022 Organisme de stage : LIGM-IMAGINE En ligne : https://hal.science/tel-03968114v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102573 A PCA-PD fusion method for change detection in remote sensing multi temporal images / Soltana Achour in Geocarto international, vol 37 n° 1 ([01/01/2022])
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Titre : A PCA-PD fusion method for change detection in remote sensing multi temporal images Type de document : Article/Communication Auteurs : Soltana Achour, Auteur ; Miloud Chikr Elmezouar, Auteur ; Nasreddine Taleb, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 196 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] fusion de données
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image panchromatique
[Termes IGN] méthode statistique
[Termes IGN] seuillage d'imageRésumé : (auteur) In remote sensing, for applications as environment monitoring, change detection based on image processing is one of the most important techniques. To reach high performance various techniques of fusion are exploited using a combination of multi-temporal, multispectral and panchromatic satellite images. A solution for handling such kind of images holds when using some simple statistical methods like the Percent Difference (PD) technique as well as the Principal Component Analysis (PCA) one. In this paper, an automatic change detection method issued from the two previous techniques is proposed and applied on multispectral and panchromatic images captured by a high resolution optical satellite. This approach is characterized by two aspects: the first one consists of the fusion of the different data and the second one performs the detection of the changes for the resulting images. The experimental results show the reasonable quantitative performance and the effectiveness of the proposed method for change detection, consisting of an automatic extraction of most of change information as well as the obtention of better results for most precision metrics consisting of an overall accuracy of up to 91% and a Kappa coefficient of up to 66%, comparing to those obtained using the simple PD and PCA techniques. Numéro de notice : A2022-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1713228 Date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1713228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99441
in Geocarto international > vol 37 n° 1 [01/01/2022] . - pp 196 - 213[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)
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Titre : Pedestrian trajectory prediction with convolutional neural networks Type de document : Article/Communication Auteurs : Simone Zamboni, Auteur ; Zekarias Tilahun Kefato, Auteur ; Sarunas Girdzijauskas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] distance euclidienne
[Termes IGN] filtre de Gauss
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] prévision à court terme
[Termes IGN] réseau social
[Termes IGN] trajet (mobilité)Résumé : (auteur) Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction. Numéro de notice : A2022-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108252 Date de publication en ligne : 13/08/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99615
in Pattern recognition > vol 121 (January 2022) . - n° 108252[article]Photogrammetric point clouds: quality assessment, filtering, and change detection / Zhenchao Zhang (2022)
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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 Planning coastal Mediterranean stone pine (Pinus pinea L.) reforestations as a green infrastructure: combining GIS techniques and statistical analysis to identify management options / Luigi Portoghesi in Annals of forest research, vol 65 n° 1 (January - June 2022)
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Titre : Planning coastal Mediterranean stone pine (Pinus pinea L.) reforestations as a green infrastructure: combining GIS techniques and statistical analysis to identify management options Type de document : Article/Communication Auteurs : Luigi Portoghesi, Auteur ; Antonio Tomao, Auteur ; Simone Bollati, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 31 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] approche hiérarchique
[Termes IGN] carte forestière
[Termes IGN] Italie
[Termes IGN] littoral méditerranéen
[Termes IGN] peuplement pur
[Termes IGN] Pinus pinea
[Termes IGN] reboisement
[Termes IGN] résilience écologique
[Termes IGN] structure de la végétation
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Mediterranean stone pine reforestations are common characteristics of the Italian Tyrrhenian coast, which mostly maintain uniform and monolayered stand structures. However, improving structural diversity is an effective climate change adaptation strategy in forest management. The aim of this study was to implement a methodology which allows distinct reforested areas such as a single green infrastructure to be managed according to the surrounding land use and the characteristics of the forest stands. 240 hectares of Mediterranean stone pine forests located along a 16 km strip of the Lazio coast (Central Italy) were mapped. Twelve attributes describing the pine stands and showing possible constraints for future management decisions were associated to each forest patch. A hierarchical cluster analysis was performed to group the pinewood patches according to their similarity level and five different groups were identified. For each group, different silvicultural methods were proposed to guide the compositional and structural evolution of the stands, in order to make them suitable for providing services required locally and increasing overall diversity at landscape scale. The results of the study highlight how coastal land uses can offer effective inputs to differentiate the management of forest systems and therefore achieve greater variety and resilience in the landscape over time. This approach is particularly useful in the case of very homogeneous stands such as the stone pine reforestations under study. Numéro de notice : A2022-798 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.15287/afr.2022.2176 Date de publication en ligne : 27/06/2022 En ligne : https://doi.org/10.15287/afr.2022.2176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101958
in Annals of forest research > vol 65 n° 1 (January - June 2022) . - pp 31 - 46[article]Potentialité de la télédétection thermique pour la modélisation climatique en milieu viticole / Gwenaël Morin (2022)
PermalinkA prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkA rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkPermalinkRemise en forme des données géographiques des biotopes en milieu ouvert du Luxembourg / Alexandre Nghien (2022)
PermalinkPermalinkPermalinkSelf-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)
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PermalinkSpatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic / Nicolas Francos in Geomatics, Natural Hazards and Risk, vol 13 (2022)
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