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Auteur Ebadat Ghanbari Parmehr |
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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)
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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 pointsRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7032 - 7043[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)
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[article]
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 donnéesRésumé : (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
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 28 - 40[article]Exemplaires(1)
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