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Global-aware siamese network for change detection on remote sensing images / Ruiqian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 199 (May 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 199 (May 2023) . - pp 61 - 72 Titre : Global-aware siamese network for change detection on remote sensing images Type de document : Article/Communication Auteurs : Ruiqian Zhang, Auteur ; Hanchao Zhang, Auteur ; Xiaogang Ning, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de sensibilité [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau neuronal siamois Résumé : (auteur) Change detection (CD) in remote sensing images is one of the most important technical options to identify changes in observations in an efficient manner. CD has a wide range of applications, such as land use investigation, urban planning, environmental monitoring and disaster mapping. However, the frequently occurring class imbalance problem brings huge challenges to the change detection applications. To address this issue, we develop a novel global-aware siamese network (GAS-Net), aiming to generate global-aware features for efficient change detection by incorporating the relationships between scenes and foregrounds. The proposed GAS-Net, consisting of the global-attention module (GAM) and foreground-awareness module (FAM) that both learns contextual relationships and enhances symbiotic relation learning between scene and foreground. The experimental results demonstrate the effectiveness and robustness of the proposed GAS-Net, achieving up to 91.21% and 95.84% F1 score on two widely used public datasets, i.e., Levir-CD and Lebedev-CD dataset. The source code is available at https://github.com/xiaoxiangAQ/GAS-Net. Numéro de notice : 2023-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.04.001 Date de publication en ligne : 05/04/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103106 [article]

Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] Chine [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] mangrove [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356 [article]

How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data / Rongfang Lyu in Sustainable Cities and Society, vol 88 (January 2023)  

Public [article]  inSustainable Cities and Society > vol 88 (January 2023) . - n° 104287 Titre : How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data Type de document : Article/Communication Auteurs : Rongfang Lyu, Auteur ; Jili Pang, Auteur ; Xiaolei Tian, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104287 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Chine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espace vert [Termes IGN] hauteur du bâti [Termes IGN] ilot thermique urbain [Termes IGN] image captée par drone [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] Leaf Area Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] optimisation (mathématiques) [Termes IGN] paysage urbain [Termes IGN] plan d'eau [Termes IGN] planification urbaine [Termes IGN] réseau bayesien [Termes IGN] semis de points [Termes IGN] température au sol Résumé : (auteur) The systematical exploration of how two-dimensional (2D) and three-dimensional (3D) features of urban landscapes influence land surface temperature (LST) is still limited. Therefore, we investigated the influence of three main urban landscapes—urban green space, impervious land, and water bodies on LST, with a particular focus on the 3D vegetation metrics of green volume (GV) and leaf area index (LAI). We used Yinchuan City, China, as a case study. We quantified the impacts of various 2D/3D metrics of the three landscape types on LST using a random forest analysis with multiple sources, including Unmanned Aerial Vehicle (UAV) and remote sensing images. We then generated a Bayesian Network (BN) model to identify the optimal configurations for each landscape type. We found that using 11 of the 31 metrics considered, our model could explain 81.8% of the observed variance in LST of Yinchuan City. Among those, water body metrics were the most important, followed by vegetation abundance, impervious land metrics, and landscape pattern of urban green space. The mean classification error of the BN model was only 22.9%. We suggest that this makes the BN model a promising support tool for urban planning with a view to urban heat island mitigation. Our findings also stress the importance of considering both 2D and 3D features when considering urban cooling strategies. Numéro de notice : A2023-007 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104287 Date de publication en ligne : 02/11/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104287 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102095 [article]

Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution / Estera Trzcina in Journal of geodesy, vol 97 n° 1 (January 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 1 (January 2023) . - n° 2 Titre : Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution Type de document : Article/Communication Auteurs : Estera Trzcina, Auteur ; Witold Rohm, Auteur ; Kamil Smolak, Auteur Année de publication : 2023 Article en page(s) : n° 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] interpolation bilinéaire [Termes IGN] modèle météorologique [Termes IGN] optimisation (mathématiques) [Termes IGN] radiosondage [Termes IGN] récepteur GNSS [Termes IGN] retard troposphérique [Termes IGN] retard troposphérique zénithal [Termes IGN] système de grille globale discrète [Termes IGN] teneur en vapeur d'eau [Termes IGN] tomographie [Termes IGN] troposphère Résumé : (auteur) Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km. Numéro de notice : A2023-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1007/s00190-022-01691-0 Date de publication en ligne : 30/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01691-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102343 [article]

Automatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103083 Titre : Automatic registration of point cloud and panoramic images in urban scenes based on pole matching Type de document : Article/Communication Auteurs : Yuan Wang, Auteur ; Yuhao Li, Auteur ; Yiping Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103083 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] appariement de formes [Termes IGN] chevauchement [Termes IGN] extraction de traits caractéristiques [Termes IGN] image panoramique [Termes IGN] image virtuelle [Termes IGN] optimisation par essaim de particules [Termes IGN] points registration [Termes IGN] recalage d'image [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile [Termes IGN] zone tampon Résumé : (auteur) Given the initial calibration of multiple sensors, the fine registration between Mobile Laser Scanning (MLS) point clouds and panoramic images is still challenging due to the unforeseen movement and temporal misalignment while collecting. To tackle this issue, we proposed a novel automatic method to register the panoramic images and MLS point clouds based on the matching of pole objects. Firstly, 2D pole instances in the panoramic images are extracted by a semantic segmentation network and then optimized. Secondly, every corresponding frustum point cloud of each pole instance is obtained by a shape-adaptive buffer region in the panoramic image, and the 3D pole object is extracted via a combination of slicing, clustering, and connected domain analysis, then all 3D pole objects are fused. Finally, 2D and 3D pole objects are re-projected onto virtual images respectively, and then fine 2D-3D correspondences are collected through maximizing pole overlapping area by Particle Swarm Optimization (PSO). The accurate extrinsic orientation parameters are acquired by the Efficient Perspective-N-Point (EPnP). The experiments indicate that the proposed method performs effectively on two challenging urban scenes with an average registration error of 2.01 pixels (with RMSE 0.88) and 2.35 pixels (with RMSE 1.03), respectively. Numéro de notice : A2022-827 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103083 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103083 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102011 [article]

Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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Reconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)  

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Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)  

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A whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)  

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An improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)  

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A fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 2022)  

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Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

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Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])  

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Prediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])  

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A general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)  

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