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Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)
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Titre : Application of digital image processing in automated analysis of insect leaf mines Type de document : Thèse/HDR Auteurs : Yee Man Theodora Cho, Auteur Editeur : York [Royaume-Uni] : University of York Année de publication : 2020 Importance : 202 p. Format : 21 x 30 cm Note générale : bibliographie
PhD thesis, Electronic Engineering, University of York, United KingdomLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Acer (genre)
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] Castanea (genre)
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] détection de contours
[Termes IGN] diagnostic foliaire
[Termes IGN] image hyperspectrale
[Termes IGN] insecte nuisible
[Termes IGN] modèle de simulation
[Termes IGN] segmentation d'image
[Termes IGN] seuillage
[Termes IGN] surveillance de la végétation
[Termes IGN] taxinomie
[Termes IGN] traitement d'imageRésumé : (auteur) Automated species identificationhas become a popular alternative to manual classification in the past few decades, as a result of advancement in digital image processing techniques and machine learning algorithms. This project aims to devise a new approach for the detection of leaf mines and fungal spots from digital images, and to investigate the possibility of monitoring the growth of leaf mines. Leaf-mining insects primarily belong to the orders of moths (Lepidoptera), flies (Diptera) and beetles (Coleoptera); or the suborders of sawflies (Symphyta) and wasps (Apocrita). Every spring and summer the larvae of leaf-mining insects feed on leaf tissues until maturity and vacate the mines as adults. As most species of leaf miners attack garden plants or crops, they are generally regarded as pests, despiterarely causing severe long-term detrimental effect on their host plants. Increase in human activities has led to the spread of these invasive species globally in recent years, and the demand for an effective classification system to monitor their distribution is rising consistently. Samples from three species of leaf-mining insects were included in this project: horse chestnut leaf miner (Cameraria ohridella), apple leaf miner (Lyonetia clerkella), and holly leaf miner (Phytomyza ilicis). Leaves with tar spots (Rhytisma acerinum)were also introduced as variations.The proposed method uses image processing techniques such as thresholding, conversion between colour spaces, edge detection, image segmentation,and morphological operations. This project also explores the use of machine learning algorithmsas analytical monitoring and predictive tools, using the growth of C. ohridellaleaf mines as an example. Note de contenu : 1- Introduction
2- Background
3- Digital image processing
4- Automated classification
5- Implementation
6- Data analysis
7- ConclusionNuméro de notice : 28552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Electronic Engineering : University of York : 2020 En ligne : https://etheses.whiterose.ac.uk/27749/1/Cho_105036528_Thesis.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97414
Titre : Applications of remote sensing in coastal areas Type de document : Monographie Auteurs : Konstantinos Topouzelis, Éditeur scientifique ; Apostolos Papakonstantinou, Éditeur scientifique ; Siman Singha, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 288 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-03928-659-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification orientée objet
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification pixellaire
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] érosion côtière
[Termes IGN] falaise
[Termes IGN] habitat (nature)
[Termes IGN] herbier marin
[Termes IGN] image PlanetScope
[Termes IGN] modèle numérique de surface
[Termes IGN] surveillance du littoralRésumé : (éditeur) Coastal areas are remarkable regions with high spatiotemporal variability. A large population is affected by their physical and biological processes—resulting from effects on tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions, such as water oxygenation and nutrients provision, seafloor and beach stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, as areas for nursery, and as refuge for several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of marine resources. Remote sensing from UAVs to spaceborne sensors is offering a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically addresses those successful applications—from local to regional scale—in coastal environments related to ecosystem productivity, biodiversity, sea level rise. Note de contenu : 1- Monitoring cliff erosion with LiDAR surveys and Bayesian network-based data analysis
2- Cubesats allow high spatiotemporal estimates of satellite-derived bathymetry
3- Comparison of Pixel- and object-based classification methods of unmanned aerial vehicle data applied to coastal dune vegetation communities: Casal Borsetti case stud
4- Capturing coastal dune natural vegetation types using a phenology-based mapping approach: The potential of Sentinel-2
5- Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra
6- Satellite observations of wind wake and associated oceanic thermal responses: A case study of Hainan Island wind wake
7- Comparison of true-color and multispectral unmanned aerial systems imagery for marine habitat mapping using object-based image analysis
8- Spatial and temporal variability of open-ocean barrier islands along the Indus Delta region
9- Characterizing and monitoring ground settlement of marine reclamation land of Xiamen New Airport, China with Sentinel-1 SAR datasets
10- Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery
11- Photon-counting Lidar: An adaptive signal detection method for different land cover types in coastal area
12- Automatic semi-global artificial shoreline subpixel localization algorithm for Landsat imagery
13- Analysis of ship detection performance with full-, compact- and dual-polarimetric SAR
14- Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI dataNuméro de notice : 28689 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03928-659-1 En ligne : https://doi.org/10.3390/books978-3-03928-659-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100128 Context-aware convolutional neural network for object detection in VHR remote sensing imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)
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[article]
Titre : Context-aware convolutional neural network for object detection in VHR remote sensing imagery Type de document : Article/Communication Auteurs : Yiping Gong, Auteur ; Zhifeng Xiao, Auteur ; Xiaowei Tan, Auteur Année de publication : 2020 Article en page(s) : pp 34 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] description multiniveau
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] prise en compte du contexte
[Termes IGN] vision par ordinateur
[Termes IGN] zone d'intérêtRésumé : (auteur) Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods. Numéro de notice : A2020-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2930246 Date de publication en ligne : 23/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2930246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94492
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 34 - 44[article]
Titre : Deep learning for semantic feature extraction in aerial imagery Type de document : Thèse/HDR Auteurs : Ananya Gupta, Auteur ; Hujun Yin, Directeur de thèse ; Simon Watson, Directeur de thèse Editeur : Manchester [Royaume-Uni] : University of Manchester Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the faculty of Science and engineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] cartographie d'urgence
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Dublin (Irlande ; ville)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multitemporelle
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routier
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] voxelIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Remote sensing provides image and LiDAR data that can be useful for a number of tasks such as disaster mapping and surveying. Deep learning (DL) has been shown to provide good results in extracting knowledge from input data sources by the means of learning intermediate representation features. However, popular DL methods require large scaled datasets for training which are costly and time-consuming to obtain. This thesis investigates semantic knowledge extraction from remote sensing data using DL methods in regimes with limited labelled data. Firstly, semantic segmentation methods are compared and analysed on the task of aerial image segmentation. It is shown that pretraining on ImageNet improves the segmentation results despite the domain shift between ImageNet images and aerial images. A framework for mapping road networks in disaster struck areas is proposed. It uses pre and post disaster imagery and labels from OpenStreetMaps (OSM), forgoing the need for costly manually labelled data. Graph-based methods are used to update the pre-existing road maps from OSM. Experiments on a disaster dataset from Palu, Indonesia show the efficacy of the proposed method. A method for semantic feature extraction from aerial imagery is proposed which is shown to work well for multitemporal high resolution image registration. These feature are able to deal with temporal variations caused by seasonal changes. Methods for tree identification in LiDAR data have been proposed to overcome the need for manually labelled data. The first method works on high density point clouds and uses certain LiDAR data attributes for tree identification, achieving almost 90% accuracy. The second uses a voxel based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees. The third method is a scaled version of PointNet++ and achieves an F_score of 82.1 on the ISPRS benchmark, comparable to the state of the art methods but with increased efficiency. Finally, saliency methods used for explainability in image analysis are extended to work on 3D point clouds and voxel-based networks to help aid explainability in this area. It is shown that edge and corner features are deemed important by these networks for classification. These features are also demonstrated to be inherently sparse and pruned easily. Note de contenu : 1- Introduction
2- Background and Literature Review
3- Aerial Image Segmentation with Open Data
4- Aerial Image Registration
5- Tree Annotations in LiDAR Data
6- 3D Point Cloud Feature Explanations
7- Conclusions and Future WorkNuméro de notice : 28302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Science and Engineering : University of Manchester : 2020 DOI : sans En ligne : https://www.research.manchester.ac.uk/portal/files/184627877/FULL_TEXT.PDF Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98051 Détection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie / Etienne Barçon (2020)
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Titre : Détection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie Type de document : Mémoire Auteurs : Etienne Barçon, Auteur Editeur : Strasbourg : Institut National des Sciences Appliquées INSA Strasbourg Année de publication : 2020 Importance : 110 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire de fin d'études d'Ingénieur INSA, spécialité TopographieLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] corrélation croisée normalisée
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] polyligne
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] seuillage d'image
[Termes IGN] trottoir
[Termes IGN] vectorisationIndex. décimale : INSAS Mémoires d'ingénieur de l'INSA Strasbourg - Topographie, ex ENSAIS Résumé : (auteur) Ce mémoire présente une méthode de détection automatique des marquages et des glissières dans un environnement autoroutier, à partir d'un nuage de points. L'approche employée utilise essentiellement des outils de traitement d'images. Le nuage de points est converti en images d’intensité et images d’altitudes par une projection verticale sur un plan. La détection des marquages au sol est effectuée par seuillage des images d’intensité. La détection des glissières s’effectue en plusieurs temps, une détection des objets linéaires en 2D puis une vectorisation 3D à partir de profils, à l’aide des images d’altitudes. Les résultats obtenus sont convaincants bien que perfectibles. La méthode mise en place est jugée généralisable à d’autres objets comme les murs et les bordures de trottoir. Note de contenu : Introduction
1- Etat de l'art
2- Développements
3- Résultats obtenus et analyse
4- Perspectives d'améliorations
ConclusionNuméro de notice : 28517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur INSAS Organisme de stage : TT Géomètres-Experts (Paris) DOI : sans En ligne : http://eprints2.insa-strasbourg.fr/4144/ Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97292 PermalinkFusion of 3D point clouds and hyperspectral data for the extraction of geometric and radiometric features of trees / Eduardo Alejandro Tusa Jumbo (2020)
PermalinkGeoreferenced measurements of building objects with their simultaneous shape detection / Edward Osada in Survey review, Vol 52 n°370 (January 2020)
PermalinkImage processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)
PermalinkPermalinkLearning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)
PermalinkPermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles (2020)
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