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Titre : Automatisation du nettoyage de nuages de points Type de document : Mémoire Auteurs : Yohan Pensier, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2016 Importance : 71 p. Format : 21 x 30 cm Note générale : Bibliographie
mastère Photogrammétrie, positionnement et mesures de déformationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] 3DReshaper
[Termes IGN] C++
[Termes IGN] contrôle qualité
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effet de bord
[Termes IGN] filtrage du bruit
[Termes IGN] lancer de rayons
[Termes IGN] modélisation 3D
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision centimétrique
[Termes IGN] réseau ferroviaire
[Termes IGN] semis de points
[Termes IGN] valeur aberranteIndex. décimale : MPPMD Mémoires du mastère spécialisé Photogrammétrie, Positionnement et Mesures de Déformation Résumé : (Auteur) La division Assistance Travaux et Topographie (ATT) de la direction d’ingénierie et projet de SNCF Réseau réalise et pilote des opérations de levé topographiques 3D (voies, gares, …). À ce titre, elle a également pour mission d’effectuer le contrôle qualité des données livrées par les différents prestataires. Afin de pouvoir répondre rapidement à ces missions de grande échelle, un nombre important de techniques émergentes est déployé en complément de la topographie traditionnelle, notamment, le scanner laser dynamique ferroporté. La donnée 3D obtenue par ce type d’appareil est exhaustive, sauf en cas de masques, mais peut contenir des artefacts de mesures (bruits, points fantômes, …). Le stage réalisé devait donc permettre de : - Intégrer les usages de nettoyages de nuages de points de SNCF Réseau ; - Proposer et implémenter des filtres mathématiques pour automatiser le nettoyage des nuages de points (détection de points aberrants, comparaison de nuages, segmentation et classification du nuage de points, …) et les mettre en oeuvre en les intégrant dans les chaînes de traitement actuelles ; - Proposer une méthodologie applicable sur des zones très étendues (environ 100 km linéaires). Note de contenu : 1. APPROCHE INITIALE
1.1 Cadre du stage
1.2. Principes du levé LIDAR en milieu ferroviaire
1.3. Évaluation de l’existant
2. NETTOYAGE SEMI-AUTOMATIQUE DES NUAGES DE POINTS STATIQUES
2.1. Le logiciel 3DReshaper
2.2. Présentation du programme « filtrage_auto »
2.3. Résultats obtenus
3. AUTOMATISATION DU NETTOYAGE DE SCANS DYNAMIQUES
3.1. Méthode mathématique retenue
3.2. Le programme « Top_Gun »
3.3. Exemple de traitement
ConclusionNuméro de notice : 22662 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Mémoire PPMD Organisme de stage : SNCF Réseau Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84007 Documents numériques
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22662_Automatisation du nettoyage de nuages de points.pdfAdobe Acrobat PDFMulti-label class assignment in land-use modelling / Hichem Omrani in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)
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Titre : Multi-label class assignment in land-use modelling Type de document : Article/Communication Auteurs : Hichem Omrani, Auteur ; Fahed Abdallah, Auteur ; Omar Charif, Auteur Année de publication : 2015 Article en page(s) : pp 1023 - 1041 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] alignement semi-dirigé
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image aérienne
[Termes IGN] Luxembourg
[Termes IGN] modélisation
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] utilisation du solRésumé : (Auteur) During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling. Numéro de notice : A2015-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1008004 En ligne : https://doi.org/10.1080/13658816.2015.1008004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78013
in International journal of geographical information science IJGIS > vol 29 n° 6 (June 2015) . - pp 1023 - 1041[article]Collaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Collaborative representation for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 1463 - 1474 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'anomalie
[Termes IGN] distance pondérée
[Termes IGN] image hyperspectrale
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] plus proche voisin, algorithme duRésumé : (Auteur) In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l2-norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost. Numéro de notice : A2015-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2343955 Date de publication en ligne : 12/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2343955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75799
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1463 - 1474[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images / Zahra Ziaei in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
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Titre : A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images Type de document : Article/Communication Auteurs : Zahra Ziaei, Auteur ; Biswajeet Pradhan, Auteur ; Shattri Bin Mansor, Auteur Année de publication : 2014 Article en page(s) : pp 554-569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification à base de connaissances
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] eCognition
[Termes IGN] image Worldview
[Termes IGN] plus proche voisin, algorithme duRésumé : (auteur) Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%). Numéro de notice : A2014-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.819039 En ligne : https://doi.org/10.1080/10106049.2013.819039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73949
in Geocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 554-569[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
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Titre : Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method Type de document : Article/Communication Auteurs : Yuan Zhou, Auteur ; Jin Chen, Auteur ; Qinghua Guo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 313 - 328 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] montagne
[Termes IGN] ombre
[Termes IGN] pixel
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] restauration d'image
[Termes IGN] valeur radiométriqueRésumé : (Auteur) Shadows in remotely sensed imagery occur when objects totally or partially occlude direct light from a source of illumination, generating great difficulty in land cover interpretation and classification because of the loss of spectral information of shaded pixels. In a mountainous environment with rough terrain, shadows are especially pronounced due to the differentiation of direct illumination between sunny and shady slopes. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. However, there are two limitations: one is that the contemporary topographic corrections have little effect on areas that have very low incidence angles and areas that are completely without direct solar illumination (cast shadow); another is that their effectiveness is restricted by the data quality and completeness, spatial resolution, and elevation accuracy of the Digital Elevation Model (DEM) data, which is not currently available in all parts of the world. Thus, noise and errors may be introduced in topographic correction during resampling and geometric registration of the target image. This paper proposes a new approach to restore the radiometric information of mountainous cast shadows using a spectral processing technique called “continuum removal” (CR) without the aid of DEM. The CR-based approach makes full use of the spectral information derived from both the shaded pixels and their neighboring nonshaded pixels of the same land cover type. Several Landsat TM images were used to assess the performance of the proposed method. Results indicated that the proposed method can effectively restore the spectral values of shaded pixels more accurately than the ATCOR_3 correction method, especially for very low incidence angle areas and cast shadows. By comparing data values of shaded pixels with nonshaded pixels (pure reference pixels) of their same class, images processed by the proposed method had the lowest average root mean square error (RMSE) between them in visible, NIR and SWIR bands, followed by the ATCOR_3 correction method and the original image. In addition, the proposed method achieved the best classification accuracy, higher than those from the original test image and the ATCOR_3 corrected image generated using 90 m or 30 m spatial resolution DEM. Therefore, the Continuum Removal method is a better alternative for restoring objects obscured by mountainous shadow when adequate DEM data are unavailable and the quality of DEM cannot satisfy the requirements of topographic correction algorithms. Numéro de notice : A2014-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2239651 En ligne : https://doi.org/10.1109/TGRS.2013.2239651 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32942
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 313 - 328[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014011A RAB Revue Centre de documentation En réserve L003 Disponible Blind evaluation of location based queries using space transformation to preserve location privacy / Ali Khshgozaran in Geoinformatica, vol 17 n° 4 (October 2013)
PermalinkPermalinkContinuous aggregate nearest neighbor queries / H. Elmongui in Geoinformatica, vol 17 n° 1 (January 2013)
PermalinkA query integrity assurance scheme for accessing outsourced spatial databases / W. Ku in Geoinformatica, vol 17 n° 1 (January 2013)
PermalinkPNN query processing on compressed trajectories / S. Shang in Geoinformatica, vol 16 n° 3 (July 2012)
PermalinkGeneralized network Voronoi diagrams: concepts, computational methods, and applications / Atsuyuki Okabe in International journal of geographical information science IJGIS, vol 22 n° 8-9 (august 2008)
PermalinkSegmentation of airborne laser scanning data using a slope adaptative neighbourhood / S. Filin in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 2 (April 2006)
PermalinkQuery processing in spatial databases containing obstacles / Jun Zhang in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)
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