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INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

Public [article]  inGPS solutions > vol 24 n° 1 (January 2020) Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458 [article]

Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7365 - 7377 Titre : Saliency-guided deep neural networks for SAR image change detection Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Xiaorui Ma, Auteur ; Xiaojun Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7365 - 7377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] saillance [Termes descripteurs IGN] télédétection en hyperfréquence Mots-clés libres : hierarchical fuzzy C-means clustering (HFCM) Résumé : (auteur) Change detection is an important task to identify land-cover changes between the acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle noise of the images can lead to false changed points, which affects the change detection performance. Besides, the supervised classifier in change detection framework requires numerous training samples, which are generally obtained by manual labeling. In this paper, a novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection. In the proposed method, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image. To obtain pseudotraining samples automatically, hierarchical fuzzy C-means (HFCM) clustering is developed to select samples with higher probabilities to be changed and unchanged. Moreover, to enhance the discrimination of sample features, DNNs based on the nonnegative- and Fisher-constrained autoencoder are applied for final detection. Experimental results on five real SAR data sets demonstrate the effectiveness of the proposed approach. Numéro de notice : A2019-536 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2913095 date de publication en ligne : 19/05/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2913095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94154 [article]

Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

Public [article]  inBaltic forestry > vol 25 n° 1 (2019) . - pp 113 - 123 Titre : Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images Type de document : Article/Communication Auteurs : Linda Gulbe, Auteur ; Aleksandrs Kozlovs, Auteur ; Janis Donis, Auteur ; Agris Tradkovs, Auteur Année de publication : 2019 Article en page(s) : pp 113 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Lettonie Résumé : (auteur) Countrywide up-to-date tree cover maps provide valuable information for planning and management purposes to investigate location of the resources and to identify afforestation and deforestation patterns. Landsat programme offers freely available satellite data with time span more than three decades and it can serve as bases for tree cover map calculation using satellite image classification; however, practical use of classification methods is limited due to lack of user-friendly solutions and complex interpretation of the results. The objective of this study is to evaluate user-friendly hybrid classification scheme for tree cover mapping in Latvia and to explore the nature of the spectral classes and consistency of the results when methodology is applied to images of different dates. Tree cover in this context means the area covered by crown of the tree, which may or may not be considered as forest according to local provisions. Tree cover is estimated using unsupervised fuzzy c-means methods with the stability check to ensure the presence of the same spectral classes in independent tests. Spectral classes are classified into two categories: tree cover and other by employing k-nearest neighbours. Such approach does not require high quality sample data and does not include user defined internal parameters of the algorithms (however, they can be specified if needed). The best overall accuracy achieved for year 2014 was 94.2% with producer's accuracy 98.7% (tree cover), 90.5% (other land cover), user's accuracy 90.0% (tree cover), 98.8% (other land cover) and kappa 0.89. Consistency studies showed high impact (within 10% of overall accuracy) of unique conditions during the image acquisition. Some of the spectral classes represent borderline case between relatively dense tree cover and other land cover types like sparse young stands. Those cases are the main threat to the consistency between the results of different dates and seasons. Numéro de notice : A2019-375 Thématique : FORET Nature : Article DOI : sans En ligne : https://www.balticforestry.mi.lt/bf/PDF_Articles/2019-25%5B1%5D/Baltic%20Forestr [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93417 [article]

Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)

Public [article]  inGPS world > vol 28 n° 3 (March 2017) . - pp 26 - 32 Titre : Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) Type de document : Article/Communication Auteurs : Haowei Xu, Auteur ; Lian Baowang, Auteur ; Charles K. Toth, Auteur ; Dorota Brzezinska, Auteur Année de publication : 2017 Article en page(s) : pp 26 - 32 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] SIFT (algorithme) Résumé : (Auteur) Lidar with its superior performance can replace GNSS in the integration solution by providing fixes for the drifting inertial measurement unit (IMU). Tests show its potential for terrain-referenced navigation due to its high accuracy, resolution, update rate and anti-jamming abilities. A novel algorithm uses scanning lidar ranging data and a reference database to calculate the navigation solution of the platform and then further fuse with the inertial navigation system (INS) output data. Numéro de notice : A2017-290 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85325 [article]

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 25 - 36 Titre : The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering Type de document : Article/Communication Auteurs : Yi Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] arbre BSP [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] partitionnement binaire [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] transformation de Karhunen-Loeve [Termes descripteurs IGN] valeur propre Résumé : (Auteur) The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60 GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement. Numéro de notice : A2016-795 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82529 [article]

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information / Alexis Comber in Geoinformatica [en ligne], vol 20 n° 3 (July - September 2016)  

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An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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Application of fuzzy combination operators to flood vulnerability assessments in Seoul, Korea / Moung-Jin Lee in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

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Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)

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An intelligent spatial proximity system using neurofuzzy classifiers and contextual information / F. Barouni in Geomatica [en ligne], vol 69 n° 3 (september 2015)  

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Panorama sur les méthodes de classification des images satellites et techniques d'amélioration de la précision de la classification / O. El Kharki in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)

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Road hierarchy with integration of attributes using fuzzy-AHP / Fatih Gülgen in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

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Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery / Alireza Hamedianfar in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)

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Significance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany / Michael Förster in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)

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Connaissance de la biodiversité végétale / Jan-Bernard Bouzillé (2014)

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