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Automatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7032 - 7043 Titre : Automatic parameter selection for intensity-based registration of imagery to LiDAR data Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7032 - 7043 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] appariement d'images [Termes IGN] appariement de données localisées [Termes IGN] densité de probabilité [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] image binaire [Termes IGN] segmentation binaire [Termes IGN] semis de points Résumé : (Auteur) Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method. Numéro de notice : A2016-923 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594294 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83327 [article]

Sensor modelling and camera calibration for close-range photogrammetry / Thomas Luhmann in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 115 (May 2016) . - pp 37 – 46 Titre : Sensor modelling and camera calibration for close-range photogrammetry Type de document : Article/Communication Auteurs : Thomas Luhmann, Auteur ; Clive Simpson Fraser, Auteur ; Hans-Gerd Maas, Auteur Année de publication : 2016 Article en page(s) : pp 37 – 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] capteur aérien [Termes IGN] capteur spatial [Termes IGN] étalonnage d'instrument [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] étalonnage de chambre métrique [Termes IGN] photogrammétrie terrestre Résumé : (auteur) Metric calibration is a critical prerequisite to the application of modern, mostly consumer-grade digital cameras for close-range photogrammetric measurement. This paper reviews aspects of sensor modelling and photogrammetric calibration, with attention being focussed on techniques of automated self-calibration. Following an initial overview of the history and the state of the art, selected topics of current interest within calibration for close-range photogrammetry are addressed. These include sensor modelling, with standard, extended and generic calibration models being summarised, along with non-traditional camera systems. Self-calibration via both targeted planar arrays and targetless scenes amenable to SfM-based exterior orientation are then discussed, after which aspects of calibration and measurement accuracy are covered. Whereas camera self-calibration is largely a mature technology, there is always scope for additional research to enhance the models and processes employed with the many camera systems nowadays utilised in close-range photogrammetry. Numéro de notice : A2016-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.10.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81689 [article]

Accurate and occlusion-robust multi-view stereo / Zhaokun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 109 (November 2015) . - pp 47 - 61 Titre : Accurate and occlusion-robust multi-view stereo Type de document : Article/Communication Auteurs : Zhaokun Zhu, Auteur ; Christos Stamatopoulos, Auteur ; Clive Simpson Fraser, Auteur Année de publication : 2015 Article en page(s) : pp 47 - 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement d'images [Termes IGN] modèle stéréoscopique [Termes IGN] ombre Résumé : (auteur) This paper proposes an accurate multi-view stereo method for image-based 3D reconstruction that features robustness in the presence of occlusions. The new method offers improvements in dealing with two fundamental image matching problems. The first concerns the selection of the support window model, while the second centers upon accurate visibility estimation for each pixel. The support window model is based on an approximate 3D support plane described by a depth and two per-pixel depth offsets. For the visibility estimation, the multi-view constraint is initially relaxed by generating separate support plane maps for each support image using a modified PatchMatch algorithm. Then the most likely visible support image, which represents the minimum visibility of each pixel, is extracted via a discrete Markov Random Field model and it is further augmented by parameter clustering. Once the visibility is estimated, multi-view optimization taking into account all redundant observations is conducted to achieve optimal accuracy in the 3D surface generation for both depth and surface normal estimates. Finally, multi-view consistency is utilized to eliminate any remaining observational outliers. The proposed method is experimentally evaluated using well-known Middlebury datasets, and results obtained demonstrate that it is amongst the most accurate of the methods thus far reported via the Middlebury MVS website. Moreover, the new method exhibits a high completeness rate. Numéro de notice : A2015-855 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.08.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.08.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79236 [article]

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity / Ebadat Ghanbari Parmehr in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 28 - 40 Titre : Automatic registration of optical imagery with 3D LiDAR data using statistical similarity Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur ; Joseph Leach, Auteur Année de publication : 2014 Article en page(s) : pp 28 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement d'images [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image optique [Termes IGN] semis de points [Termes IGN] similitude [Termes IGN] superposition d'images [Termes IGN] superposition de données Résumé : (Auteur) The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications. Numéro de notice : A2014-082 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.015 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32987 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic extraction of building roofs using LIDAR data and multispectral imagery / Mohammad Awrangjeb in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 1 - 18 Titre : Automatic extraction of building roofs using LIDAR data and multispectral imagery Type de document : Article/Communication Auteurs : Mohammad Awrangjeb, Auteur ; Chunsun Zhang, Auteur ; Clive Simpson Fraser, Auteur Année de publication : 2013 Article en page(s) : pp 1 - 18 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification dirigée [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multibande [Termes IGN] modèle numérique de terrain [Termes IGN] toit Résumé : (Auteur) Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modelling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a ‘ground mask’. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines are extracted from the grey-scale version of the orthoimage and then classified into several classes such as ‘ground’, ‘tree’, ‘roof edge’ and ‘roof ridge’ using the ground mask and colour and texture information from the orthoimagery. During segmentation of the non-ground LIDAR points, the lines from the latter two classes are used as baselines to locate the nearby LIDAR points of the neighbouring planes. For each plane a robust seed region is thereby defined using the nearby non-ground LIDAR points of a baseline and this region is iteratively grown to extract the complete roof plane. Finally, a newly proposed rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method can successfully remove vegetation and so offers high extraction rates. Numéro de notice : A2013-486 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.05.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32624 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic camera calibration in close range photogrammetry / Clive Simpson Fraser in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)  

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Building detection in complex scenes thorough effective separation of buildings from trees / M. Awrangjeb in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 7 (July 2012)    

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Building detection in complex thorough effective separation of buildings from trees / M. Awrangjeb in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 7 (July 2012)  

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Calibration of long focal length cameras in close range photogrammetry / Christos Stamatopoulos in Photogrammetric record, vol 26 n° 135 (September - November 2011)  

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Georeferencing performance of THEOS satellite imagery / S. Liu in Photogrammetric record, vol 26 n° 134 (June - August 2011)  

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Precise georeferencing of long strips of ALOS imagery / Clive Simpson Fraser in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

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Automatic detection of residential building using LIDAR data and multispectral imagery / M. Awrangjeb in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 5 (September - October 2010)  

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Interest operators for feature-based matching in close range photogrammetry / I. Jazayeri in Photogrammetric record, vol 25 n° 129 (March - May 2010)  

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A hybrid measurement approach for close-range photogrammetry / Clive Simpson Fraser in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 3 (May - June 2009)  

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Digital surface models from high-resolution satellite imagery / J. Poon in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)  

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