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Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning / Kim Lowell in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)
[article]
Titre : Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur ; Anthony Lyons, Auteur Année de publication : 2021 Article en page(s) : pp 1592 - 1610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] bathymétrie laser
[Termes IGN] données lidar
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] hydrographie
[Termes IGN] lever bathymétrique
[Termes IGN] semis de pointsRésumé : (auteur) The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata – ‘point attribute data’ (PAD) – analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less than 20 m) bathymetry from lidar point clouds. Extreme gradient boosting (XGB) models relating PAD to an existing bathymetry/not bathymetry classification were fitted and evaluated for four areas near the Florida Keys. The PAD examined include ‘pulse specific’ information such as the return intensity and PAD describing flight path consistency. The R2 values for the XGB models were between 0.34 and 0.74. Global classification accuracies were above 80% although this reflected a sometimes extreme Bathy/NotBathy imbalance that inflated global accuracy. This imbalance was mitigated by employing a probability decision threshold (PDT) that equalizes the true positive (Bathy) and true negative (NotBathy) rates. It was concluded that 1) the strength of the bathymetric signal in the PAD should be sufficient to increase accuracy of density-based lidar point cloud bathymetry extraction methods and 2) ML can successfully model the relationship between the PAD and the Bathy/NotBathy classification. A method is also presented to examine the spatial and feature-space distribution of errors that will facilitate quality assurance and continuous improvement. Numéro de notice : A2021-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1867147 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/13658816.2020.1867147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98061
in International journal of geographical information science IJGIS > vol 35 n° 8 (August 2021) . - pp 1592 - 1610[article]Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)
[article]
Titre : Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture Type de document : Article/Communication Auteurs : Pashrant K. Srivastava, Auteur ; George P. Petropoulos, Auteur ; Rajendra Prasad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme génétique
[Termes IGN] Angleterre
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] ensachage
[Termes IGN] humidité du sol
[Termes IGN] image SMOS
[Termes IGN] régression des moindres carrés partielsRésumé : (auteur) Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use. Numéro de notice : A2021-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080507 Date de publication en ligne : 27/07/2021 En ligne : https://doi.org/10.3390/ijgi10080507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98220
in ISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 507[article]Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] Google Earth Engine
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 Date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Single annotated pixel based weakly supervised semantic segmentation under driving scenes / Xi Li in Pattern recognition, vol 116 (August 2021)
[article]
Titre : Single annotated pixel based weakly supervised semantic segmentation under driving scenes Type de document : Article/Communication Auteurs : Xi Li, Auteur ; Huimin Ma, Auteur ; Sheng Yi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 107979 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Semantic segmentation tasks based on weakly supervised conditions have been put forward to achieve a lightweight labeling process. For simple images that only include a few categories, research based on image-level annotations has achieved acceptable performance. However, when facing complex scenes, since image contains a large number of classes, it becomes challenging to learn visual appearance based on image tags. In this case, image-level annotations are not useful in providing information. Therefore, we set up a new task in which a single annotated pixel is provided for each category in a whole dataset. Based on the more lightweight and informative condition, a three step process is built for pseudo labels generation, which progressively implements each class’ optimal feature representation, image inference, and context-location based refinement. In particular, since high-level semantics and low-level imaging features have different discriminative abilities for each class under driving scenes, we divide categories into “object” or “scene” and then provide different operations for the two types separately. Further, an alternate iterative structure is established to gradually improve segmentation performance, which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process. Experiments on the Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation tasks under complex driving scenes. Numéro de notice : A2021-985 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.patcog.2021.107979 En ligne : https://doi.org/10.1016/j.patcog.2021.107979 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101354
in Pattern recognition > vol 116 (August 2021) . - n° 107979[article]Structure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Structure-aware indoor scene reconstruction via two levels of abstraction Type de document : Article/Communication Auteurs : Hao Fang, Auteur ; Cihui Pan, Auteur ; Hui Huang, Auteur Année de publication : 2021 Article en page(s) : pp 155 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image optique
[Termes IGN] maillage
[Termes IGN] maille triangulaire
[Termes IGN] niveau d'abstraction
[Termes IGN] polygone
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] scène intérieureRésumé : (auteur) In this paper, we propose a novel approach that reconstructs the indoor scene in a structure-aware manner and produces two meshes with different levels of abstraction. To be precise, we start from the raw triangular mesh of indoor scene and decompose it into two parts: structure and non-structure objects. On the one hand, structure objects are defined as significant permanent parts in the indoor environment such as floors, ceilings and walls. In the proposed algorithm, structure objects are abstracted by planar primitives and assembled into a polygonal structure mesh. This step produces a compact structure-aware watertight model that decreases the complexity of original mesh by three orders of magnitude. On the other hand, non-structure objects are movable objects in the indoor environment such as furniture and interior decoration. Meshes of these objects are repaired and simplified according to their relationship with respect to structure primitives. Finally, the union of all the non-structure meshes and structure mesh comprises the scene mesh. Note that structure mesh and scene mesh preserve various levels of abstraction and can be used for different applications according to user preference. Our experiments on both LIDAR and RGBD data scanned from simple to large scale indoor scenes indicate that the proposed framework generates structure-aware results while being robust and scalable. It is also compared qualitatively and quantitatively against popular mesh approximation, floorplan generation and piecewise-planar surface reconstruction methods to demonstrate its performance. Numéro de notice : A2021-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.007 Date de publication en ligne : 23/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98119
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 155 - 170[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkUnsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)PermalinkVehicle detection in very-high-resolution remote sensing images based on an anchor-free detection model with a more precise foveal area / Xungen Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkDetail injection-based deep convolutional neural networks for pansharpening / Liang-Jian Deng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkUnsupervised denoising for satellite imagery using wavelet directional cycleGAN / Shaoyang Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkComparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])PermalinkAn adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)PermalinkCNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)PermalinkDEM- and GIS-based analysis of soil erosion depth using machine learning / Kieu Anh Nguyen in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)Permalink