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A relation-augmented embedded graph attention network for remote sensing object detection / Shu Tian in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : A relation-augmented embedded graph attention network for remote sensing object detection Type de document : Article/Communication Auteurs : Shu Tian, Auteur ; Lihong Kang, Auteur ; Xiangwei Xing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1000718 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] graphe
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] relation sémantique
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal de graphes
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method. Numéro de notice : A2022-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3073269 Date de publication en ligne : 18/05/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3073269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101788
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 1000718[article]Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds / Elena Belcore in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)
[article]
Titre : Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds Type de document : Article/Communication Auteurs : Elena Belcore, Auteur ; Melissa Latella, Auteur Année de publication : 2022 Article en page(s) : pp 644 - 655 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte de la végétation
[Termes IGN] densité de la végétation
[Termes IGN] détection d'objet
[Termes IGN] forêt ripicole
[Termes IGN] houppier
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) In recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine-scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost-effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV-photogrammetric datasets in a two fold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density-based method, which achieved accuracy values (0.76F-score) consistent with the traditional methods (0.49–0.80F-score range) but was less affected by under- and over-fitting. Secondly, the potential improvement of working on intra-annual multi-temporal datasets was assessed by applying the density-based approach to seven different scenarios, three of which were constituted by single-epoch datasets and the remaining given by the joining of the others. The F-score increased from 0.67 to 0.76 when passing from single- to multi-epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi-temporal acquisitions when performing individual crown detection from photogrammetric data. Numéro de notice : A2022-879 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.267 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1002/rse2.267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102193
in Remote sensing in ecology and conservation > vol 8 n° 5 (October 2022) . - pp 644 - 655[article]Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
[article]
Titre : Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling Type de document : Article/Communication Auteurs : Han Hu, Auteur ; Xinrong Liang, Auteur ; Yulin Ding, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 215 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] échantillonnage de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] façade
[Termes IGN] fenêtre (bâtiment)
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] photographie aérienne oblique
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Deep learning methods are typically data-hungry and require many labelled samples. Unfortunately, the amount of effort required to label the data has significantly hindered the application of deep learning methods, especially in 3D modelling tasks requiring heterogeneous samples. This paper proposes a semi-supervised adversarial recognition strategy embedded in the inverse procedural modelling engine to reduce data annotation costs for learning to model 3D façades. Beginning with textured level-of-details models, we use convolutional neural networks to recognise the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into the procedural grammar. A simple procedural engine is built inside off-the-shelf 3D modelling software, producing fine-grained window geometries. To obtain a useful model from a few labelled samples, we leverage a generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy exploits the unlabelled data to stabilise the training phase. Experiments using publicly available façade image datasets reveal that the proposed methods can improve classification accuracy and parameter estimation by approximately 10% and 50%, respectively, under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different façade styles. Numéro de notice : A2022-666 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.014 Date de publication en ligne : 30/08/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101528
in ISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 215 - 231[article]The iterative convolution–thresholding method (ICTM) for image segmentation / Dong Wang in Pattern recognition, vol 130 (October 2022)
[article]
Titre : The iterative convolution–thresholding method (ICTM) for image segmentation Type de document : Article/Communication Auteurs : Dong Wang, Auteur ; Xiaoping Wang, Auteur Année de publication : 2022 Article en page(s) : n° 108794 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] contour
[Termes IGN] convergence
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à haute résolution
[Termes IGN] itération
[Termes IGN] segmentation d'image
[Termes IGN] seuillageRésumé : (auteur) Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The objective functional usually consists of a fidelity term and a regularization term. Because objective functionals may vary from different types of images, developing an efficient, simple, and general numerical method to minimize them has become increasingly vital. However, many existing methods are model-based, converge relatively slowly, or involve complicated techniques. In this paper, we develop a novel iterative convolution–thresholding method (ICTM) that is simple, efficient, and applicable to a wide range of variational models for image segmentation. In ICTM, the interface between two different segment domains is implicitly represented by the characteristic functions of domains. The fidelity term is usually written into a linear functional of the characteristic functions, and the regularization term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution–thresholding method to minimize the approximate energy. The method has the energy-decaying property, and thus the unconditional stability is theoretically guaranteed. Numerical experiments show that the method is simple, easy to implement, robust, and applicable to various image segmentation models. Numéro de notice : A2022-779 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.patcog.2022.108794 Date de publication en ligne : 14/05/2022 En ligne : https://doi.org/10.1016/j.patcog.2022.108794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101857
in Pattern recognition > vol 130 (October 2022) . - n° 108794[article]Comparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)
[article]
Titre : Comparison of deep neural networks in detecting field grapevine diseases using transfer learning Type de document : Article/Communication Auteurs : Antonios Morellos, Auteur ; Xanthoula Eirini Pantazi, Auteur ; Charalampos Paraskevas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4648 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Grèce
[Termes IGN] jeu de données
[Termes IGN] maladie cryptogamique
[Termes IGN] maladie phytosanitaire
[Termes IGN] viticultureRésumé : (auteur) Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%. Numéro de notice : A2022-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14184648 Date de publication en ligne : 17/09/2022 En ligne : https://doi.org/10.3390/rs14184648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101794
in Remote sensing > vol 14 n° 18 (September-2 2022) . - n° 4648[article]Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)PermalinkThe FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (September-15 2022)PermalinkAnalytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)PermalinkAssessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)PermalinkBenchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)PermalinkA boundary-based ground-point filtering method for photogrammetric point-cloud data / Seyed Mohammad Ayazi in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)PermalinkCartographic enclosure and urban cadastral mapping in the Ethiopian Somali capital / Romy Emmenegger in Cartographica, vol 57 n° 3 (September 2022)PermalinkCharacteristics of augmented map research from a cartographic perspective / Yi Cheng in Cartography and Geographic Information Science, Vol 49 n° 5 (September 2022)PermalinkCrowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])PermalinkDeep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)Permalink