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Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 150 - 158 Titre : Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2020 Article en page(s) : pp 150 - 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] extraction de points [Termes descripteurs IGN] filtre adaptatif [Termes descripteurs IGN] khi carré [Termes descripteurs IGN] pondération [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] zone urbaine Résumé : (auteur) The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM. Numéro de notice : A2020-079 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1532704 date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1532704 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94642 [article]

Micro-tasking as a method for human assessment and quality control in a geospatial data import / Atle Frenvik Sveen in Cartography and Geographic Information Science, vol 47 n° 2 (February 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 2 (February 2020) . - pp 141 - 152 Titre : Micro-tasking as a method for human assessment and quality control in a geospatial data import Type de document : Article/Communication Auteurs : Atle Frenvik Sveen, Auteur ; Anne Sofie Strom Erichsen, Auteur ; Terje Midtbo, Auteur Année de publication : 2020 Article en page(s) : pp 141 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] chevauchement [Termes descripteurs IGN] contrôle qualité [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] import de données [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] production participative Résumé : (auteur) Crowd-sourced geospatial data can often be enriched by importing open governmental datasets as long as they are up-to date and of good quality. Unfortunately, merging datasets is not straight forward. In the context of geospatial data, spatial overlaps pose a particular problem, as existing data may be overwritten when a naïve, automated import strategy is employed. For example: OpenStreetMap has imported over 100 open geospatial datasets, but the requirement for human assessment makes this a time-consuming process which requires experienced volunteers or training. In this paper, we propose a hybrid import workflow that combines algorithmic filtering with human assessment using the micro-tasking method. This enables human assessment without the need for complex tools or prior experience. Using an online experiment, we investigated how import speed and accuracy is affected by volunteer experience and partitioning of the micro-task. We conclude that micro-tasking is a viable method for massive quality assessment that does not require volunteers to have prior experience working with geospatial data. Numéro de notice : A2020-058 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1659187 date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1080/15230406.2019.1659187 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94575 [article]

Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 953 - 973 Titre : Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model Type de document : Article/Communication Auteurs : Xiaoping Wang, Auteur ; Fei Zhang, Auteur ; Hsiang-Te Kung, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 953 - 973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] algorithme de fraction [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] état du sol [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] SEL [Termes descripteurs IGN] sol salin [Termes descripteurs IGN] zone sèche Résumé : (auteur) The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (κ) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions. Numéro de notice : A2020-213 Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1654142 date de publication en ligne : 14/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1654142 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94898 [article]

Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 650 - 665 Titre : Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network Type de document : Article/Communication Auteurs : Yuxing Zhao, Auteur ; Yue Li, Auteur ; Baojun Yang, Auteur Année de publication : 2020 Article en page(s) : pp 650 - 665 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] approche géométrique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] désert [Termes descripteurs IGN] enregistrement de données [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre passe-bande [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] lutte contre le bruit [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] séisme Résumé : (auteur) Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method. Numéro de notice : A2020-076 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2938836 date de publication en ligne : 24/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2938836 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94608 [article]

Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

Public [article]  inGeodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 395 - 414 Titre : Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey Type de document : Article/Communication Auteurs : Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2019 Article en page(s) : pp 395 - 414 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] convolution (signal) [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] test de performance [Termes descripteurs IGN] Turquie Résumé : (Auteur) A light detection and ranging (lidar) system is one of the most important technologies used for generating digital terrain models (DTMs). The point cloud data obtained by these systems consist of data gathered from ground and nonground features. To create a DTM with high resolution and accuracy, ground and nonground data must be separated. Numerous filtering algorithms have been developed for this purpose. The aim of this study was testing the filtering performance of six different filtering algorithms in four different test areas with different land cover were selected that had topographical features and characteristics. The algorithms were adaptive triangulated irregular network (ATIN), elevation threshold with an expand window (ETEW), maximum local slope (MLS), progressive morphology (PM), iterative polynomial fitting (IPF), and multiscale curvature classification (MCC) algorithms. In the results, all the filters performed well on a smooth surface and produced more errors in complex urban areas and rough terrain with dense vegetation. The IPF filtering algorithm generated the best results for the first three test areas (smooth landscape, urban areas and agricultural areas), while ETEW performed best in the fourth test area (steep areas with dense vegetation and infrastructure). Numéro de notice : A2019-502 Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.395-414 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.395-414 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93784 [article]

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