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Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products / Ankit Sagar in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)
[article]
Titre : Multisource forest inventories: A model-based approach using k-NN to reconcile forest attributes statistics and map products Type de document : Article/Communication Auteurs : Ankit Sagar , Auteur ; Cédric Vega , Auteur ; Olivier Bouriaud , Auteur ; Christian Piedallu, Auteur ; Jean-Pierre Renaud , Auteur Année de publication : 2022 Projets : LUE / Université de Lorraine, ARBRE / AgroParisTech (2007 -), DEEPSURF / Pironon, Jacques Article en page(s) : pp 175 - 188 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] données allométriques
[Termes IGN] données lidar
[Termes IGN] image Landsat-8
[Termes IGN] inventaire forestier national (données France)
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question. We address this problem using an original approach relying on a model-based inference framework and k-nearest neighbours (k-NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy. The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k-NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration’s relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k-NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes. The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes. Numéro de notice : A2022-629 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101495
in ISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 175 - 188[article]Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)
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Titre : Novel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud Type de document : Article/Communication Auteurs : Jie Yang, Auteur ; Xiaorong Wen, Auteur ; Qiulai Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] branche (arbre)
[Termes IGN] C++
[Termes IGN] Chine
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] itération
[Termes IGN] modélisation de la forêt
[Termes IGN] semis de points
[Termes IGN] squelettisationRésumé : (auteur) More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes. Numéro de notice : A2022-835 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13101534 Date de publication en ligne : 17/09/2022 En ligne : https://doi.org/10.3390/f13101534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102032
in Forests > vol 13 n° 10 (October 2022) . - n° 1534[article]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]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]Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms / Yadong Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)
[article]
Titre : Single-image super-resolution for remote sensing images using a deep generative adversarial network with local and global attention mechanisms Type de document : Article/Communication Auteurs : Yadong Li, Auteur ; Sébastien Mavromatis, Auteur ; Feng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3000224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image isolée
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] reconstruction d'image
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth’s surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines L1 pixel loss, L1 perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method. Numéro de notice : A2022-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3093043 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3093043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101789
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 3000224[article]Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions / Di Zhu in Geoinformatica, vol 26 n° 4 (October 2022)PermalinkSpatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)PermalinkThe iterative convolution–thresholding method (ICTM) for image segmentation / Dong Wang in Pattern recognition, vol 130 (October 2022)PermalinkA comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers / Qasim Khan in Geocarto international, vol 37 n° 20 ([20/09/2022])PermalinkComparison of deep neural networks in detecting field grapevine diseases using transfer learning / Antonios Morellos in Remote sensing, vol 14 n° 18 (September-2 2022)PermalinkDevelopment of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkForest 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)PermalinkRegional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe / Géraud de Streel in Forest ecology and management, vol 520 (September-15 2022)PermalinkAdaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)PermalinkAn improved multi-task pointwise network for segmentation of building roofs in airborne laser scanning point clouds / Chaoquan Zhang in Photogrammetric record, vol 37 n° 179 (September 2022)Permalink