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Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
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Titre : Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data Type de document : Article/Communication Auteurs : Mahdi Moalla, Auteur ; Hichem Frigui, Auteur ; Andrew Karem, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7022 - 7034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] cible cachée
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données radar
[Termes IGN] image radar moirée
[Termes IGN] mine antipersonnel
[Termes IGN] radar pénétrant GPR
[Termes IGN] réseau neuronal récurrent
[Termes IGN] sous-solRésumé : (auteur) We propose discrimination algorithms for buried threat detection (BTD) that exploit deep convolutional neural networks (CNNs) and recurrent neural networks (RNN) to analyze 2-D GPR B-scans in the down-track (DT) and cross-track (CT) directions as well as 3-D GPR volumes. Instead of imposing a specific model or handcrafted features, as in most existing detectors, we use large real GPR data collections and data-driven approaches that learn: 1) features characterizing buried explosive objects (BEOs) in 2-D B-scans, both in the DT and CT directions; 2) the variation of the CNN features learned in a fixed 2-D view across the third dimension; and 3) features characterizing BEOs in the original 3-D space. The proposed algorithms were trained and evaluated using large experimental GPR data covering a surface area of 120 000 m 2 from 13 different lanes across two U.S. test sites. These data include a diverse set of BEOs consisting of varying shapes, metal content, and underground burial depths. We provide some qualitative analysis of the proposed algorithms by visually comparing their performance and consistency along different dimensions and visualizing typical features learned by some nodes of the network. We also provide quantitative analysis that compares the receiver operating characteristics (ROCs) obtained using the proposed algorithms with those obtained using existing approaches based on CNN as well as traditional learning. Numéro de notice : A2020-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978763 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978763 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95914
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7022 - 7034[article]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 (October 2020)
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Titre : Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Cuizhen Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 329 - 342 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] contour
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] jeu de données
[Termes IGN] Kiangsi (Chine)
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] taille du jeu de donnéesRésumé : (auteur) Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. While existing land cover data can be converted to large training sets, the size of training data set needs to be carefully considered. In this paper, we used different portions of a high-resolution land cover map to produce different sizes of training sets to train DCNNs (SegNet and U-Net) and then quantitatively evaluated the impact of training set size on the performance of the trained DCNN. We also introduced a new metric, Edge-ratio, to assess the performance of DCNN in maintaining the boundary of land cover objects. Based on the experiments, we document the relationship between the segmentation accuracy and the size of the training set, as well as the nonstationary accuracies among different land cover types. The findings of this paper can be used to effectively tailor the existing land cover data to training sets, and thus accelerate the assessment and employment of deep learning techniques for high-resolution land cover map extraction. Numéro de notice : A2020-800 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1803402 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1803402 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96723
in Annals of GIS > vol 26 n° 4 (October 2020) . - pp 329 - 342[article]Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])
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Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] canopée
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] séparateur à vaste marge
[Termes IGN] spectroradiomètreRésumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967
in Geocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020101 RAB Revue Centre de documentation En réserve L003 Disponible Compensation of geometric parameter errors for terrestrial laser scanner by integrating intensity correction / Wanli Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
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Titre : Compensation of geometric parameter errors for terrestrial laser scanner by integrating intensity correction Type de document : Article/Communication Auteurs : Wanli Liu, Auteur ; Shuaishuai Sun, Auteur ; Zhixiong Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7483 - 7495 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse harmonique
[Termes IGN] angle d'incidence
[Termes IGN] compensation
[Termes IGN] erreur de mesure
[Termes IGN] erreur géométrique
[Termes IGN] erreur instrumentale
[Termes IGN] fonction spline d'interpolation
[Termes IGN] modèle mathématique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) The accuracy of geometric parameters (mainly referred to the incidence angle and measuring distance) in a terrestrial laser scanner (TLS) is not only influenced by the TLS intrinsic systematic instrumental error but also the extrinsic received intensity data. However, the current error compensation methods for geometric parameters mainly focus on the calibration of TLS intrinsic systematic instrumental error and rarely consider the extrinsic intensity data correction. For this reason, this article presents a new method integrating the TLS intrinsic systematic instrumental error calibration and extrinsic intensity data correction to compensate the TLS geometric parameter error. The error compensation procedure is implemented as follows. First, the error compensation mathematical model integrated with TLS intrinsic systematic instrumental error calibration parameters and extrinsic intensity data correction coefficient is established. Second, the hybrid harmonic analysis (HA) and the adaptive wavelet neural network (AWNN) algorithm are proposed to calculate the TLS incidence angle error compensation values. Subsequently, the cubic spline interpolation (CSI) is applied to compute the measuring distance error compensate values. Finally, the TLS (model FARO Focus S150) and the hemispherical angle calibration instrument were used to evaluate the proposed compensation method. The experimental results demonstrate that the geometric parameters are significantly influenced by the intensity data received from TLS, and the proposed method can effectively improve the overall accuracy of the TLS incidence angle and measuring distance. Numéro de notice : A2020-602 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984885 Date de publication en ligne : 15/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95957
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7483 - 7495[article]Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
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Titre : Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones Type de document : Article/Communication Auteurs : Xun Liang, Auteur ; Xiaoping Liu, Auteur ; Guangliang Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1930 - 1952 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aide à la décision
[Termes IGN] analyse de groupement
[Termes IGN] automate cellulaire
[Termes IGN] Chine
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] croissance urbaine
[Termes IGN] modèle de simulation
[Termes IGN] planification urbaine
[Termes IGN] zone d'activité économiqueRésumé : (auteur) Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs. Numéro de notice : A2020-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1741591 Date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1741591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95668
in International journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1930 - 1952[article]Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
PermalinkA framework for group converging pattern mining using spatiotemporal trajectories / Bin Zhao in Geoinformatica, vol 24 n° 4 (October 2020)
PermalinkGround-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
PermalinkHierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)
PermalinkA machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements / Bikram Koirala in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
PermalinkMachine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)
PermalinkMultiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
PermalinkNetwork-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
PermalinkOpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)
PermalinkSee the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)
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