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Improving the Fagacées growth model with an expanded common beech (Fagus sylvatica L.) data series from France and Germany / Gilles Le Moguédec in Annals of Forest Science [en ligne], vol 78 n° 4 (December 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 4 (December 2021) . - n° 84 Titre : Improving the Fagacées growth model with an expanded common beech (Fagus sylvatica L.) data series from France and Germany Type de document : Article/Communication Auteurs : Gilles Le Moguédec, Auteur ; Sidonie Artru, Auteur ; Axel Albrecht, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 84 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie [Termes IGN] Fagus sylvatica [Termes IGN] forêt équienne [Termes IGN] France (administrative) [Termes IGN] hauteur des arbres [Termes IGN] jeu de données [Termes IGN] modèle de croissance végétale [Termes IGN] modélisation de la forêt [Termes IGN] Quercus (genre) [Termes IGN] République fédérale d'Allemagne [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message: The Fagacées growth model was originally designed for application in the Northern half of France. It is a robust model with potential applicability to a larger area, though this potential has not yet been verified. We added new data to the original parameterization data set and our results show that the Fagacées formalism can be generalized. Context: The Fagacées growth and yield model was designed for the management of pure even-aged stands of European beech and served as a prototype to build models for other tree species. Aims: The objective of this study was to improve the growth components of the Fagacées model with additional data from North-Western France to South-Western Germany. Material and methods: Our model was calibrated on several forest inventory data sets. The first one (F) is the original data set that was used to elaborate the equations in the Fagacées model. The second one (F+) is the original data set extended with additional measurements on the same sites and on new sites in Northern France. The third (G) adds complementary data from a forest network in Southwestern Germany. The last one (A) is the aggregate of all these data sets. Results: Fitting the original model equations on the extended F+ dataset led us to modify the equation for stand basal area increment. This new equation also fit the German dataset well. The other equations could be applied to all datasets, some with the same parameter values and some after recalibrating according to the dataset. Conclusion: We conclude that the general form of the model’s equations is appropriate for application to other regions, but that a recalibration of the equations is preferable in order to reflect local conditions. The advantage of our approach is that fewer data are required to recalibrate an existing equation than to establish an entirely new one. Numéro de notice : A2021-695 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01086-9 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1007/s13595-021-01086-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98525 [article]

Orbit error removal in InSAR/MTInSAR with a patch-based polynomial model / Yanan Du in International journal of applied Earth observation and geoinformation, vol 102 (October 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 102 (October 2021) . - n° 102438 Titre : Orbit error removal in InSAR/MTInSAR with a patch-based polynomial model Type de document : Article/Communication Auteurs : Yanan Du, Auteur ; Hai Qiang Fu, Auteur ; Lin Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 102438 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] interferométrie différentielle [Termes IGN] jeu de données [Termes IGN] modèle d'erreur [Termes IGN] orbitographie [Termes IGN] transformation polynomiale Résumé : (auteur) The orbit error caused by the inaccuracy of the orbit state vector can lead to fringes in differential interferograms, which can impede the estimation of deformation in differential SAR interferometry (DInSAR) applications. Usually, a set of polynomial coefficients for an entire SAR image is obtained for orbit error removal. However, the orbit error plane is influenced by overfitting in the case that the SAR satellites do not have a precise orbit. In this paper, a patch-based polynomial method is proposed to fit the orbit error plane. The new method divides an SAR image into several overlapping patches in the azimuth and range directions. Every patch obtains its own polynomial coefficients, and an iterative least-square method is used to mosaic the orbit plane. This method is tested and validated via a simulated dataset and then applied to ALOS1/2 PALSAR and Sentinel-1A datasets. The accuracy of deformation is evaluated by in situ GPS datasets. The results show that the patch-based method can fit the orbit phase plane more accurately than the traditional polynomial model with millimeter-level displacement improvement, especially in the margin areas of ALOS1/2 and for the wide-coverage Sentinel-1A datasets. Moreover, in the MTInSAR parameter calculations, the new method improves the accuracy of mean velocity calculations for ALOS1 time series, with a reduction of RMSE from 4.47 mm/yr to 3.17 mm/yr. Additionally, the new method reduces the spatial correlation of the residual topographic phase, with a mean value reduction from 0.32 m to 0.13 m. Numéro de notice : A2021-687 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102438 En ligne : https://doi.org/10.1016/j.jag.2021.102438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98419 [article]

Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 6 (June 2021) . - pp 1593 - 1610 Titre : Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy Type de document : Article/Communication Auteurs : Florian Scheidegger, Auteur ; Roxana Istrate, Auteur ; Giovanni Mariani, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1593 - 1610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] architecture de réseau [Termes IGN] classification par nuées dynamiques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] distance de Fréchet [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données [Termes IGN] précision de la classification [Termes IGN] processeur graphique Résumé : (auteur) In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision toward a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 97× faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search toward promising neural network configurations. Numéro de notice : A2021-533 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01922-5 Date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01922-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97993 [article]

Refinement of interferometric SAR parameters using digital terrain model as an external reference / Jyunpei Uemoto in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 34 - 43 Titre : Refinement of interferometric SAR parameters using digital terrain model as an external reference Type de document : Article/Communication Auteurs : Jyunpei Uemoto, Auteur Année de publication : 2021 Article en page(s) : pp 34 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] ajustement de paramètres [Termes IGN] empreinte [Termes IGN] hauteur (coordonnée) [Termes IGN] hauteur des arbres [Termes IGN] hauteur du bâti [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] jeu de données [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] point d'appui [Termes IGN] radar aéroporté à visée latérale Résumé : (auteur) Numéro de notice : A2021-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.017 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97309 [article]

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A shape transformation-based dataset augmentation framework for pedestrian detection / Zhe Chen in International journal of computer vision, vol 129 n° 4 (April 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 4 (April 2021) . - pp 1121 - 1138 Titre : A shape transformation-based dataset augmentation framework for pedestrian detection Type de document : Article/Communication Auteurs : Zhe Chen, Auteur ; Wanli Ouyang, Auteur ; Tongliang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1121 - 1138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage par transformation [Termes IGN] apprentissage profond [Termes IGN] déformation d'objet [Termes IGN] détection de piéton [Termes IGN] jeu de données [Termes IGN] synthèse d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes. Accordingly, we propose the Shape Transformation-based Dataset Augmentation (STDA) framework. The proposed framework is composed of two subsequent modules, i.e. the shape-guided deformation and the environment adaptation. In the first module, we introduce a shape-guided warping field to help deform the shape of a real pedestrian into a different shape. Then, in the second stage, we propose an environment-aware blending map to better adapt the deformed pedestrians into surrounding environments, obtaining more realistic-llooking pedestrians and more beneficial augmentation results for pedestrian detection. Extensive empirical studies on different pedestrian detection benchmarks show that the proposed STDA framework consistently produces much better augmentation results than other pedestrian synthesis approaches using low-quality pedestrians. By augmenting the original datasets, our proposed framework also improves the baseline pedestrian detector by up to 38% on the evaluated benchmarks, achieving state-of-the-art performance. Numéro de notice : A2021-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01412-0 Date de publication en ligne : 09/01/2021 En ligne : https://doi.org/10.1007/s11263-020-01412-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97606 [article]

Anti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([30/03/2021])  

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Semi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)  

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Unifying remote sensing image retrieval and classification with robust fine-tuning / Dimitri Gominski (2021)  

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Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (December 2020)  

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High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Homogenizing GPS integrated water vapor time series: Benchmarking break detection methods on synthetic data sets / Roeland Van Malderen in Earth and space science, vol 7 n° 5 (May 2020)  

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Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

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A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images / Xiaohui Ding in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

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