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Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game / Dawei Zai in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 15 - 29 Titre : Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game Type de document : Article/Communication Auteurs : Dawei Zai, Auteur ; Jonathan Li, Auteur ; Yulan Guo, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 15 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] enregistrement de données [Termes IGN] matrice de covariance [Termes IGN] semis de points [Termes IGN] télémétrie laser terrestre [Termes IGN] théorie des jeux Résumé : (Auteur) It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework. Numéro de notice : A2017-729 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88426 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017121	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017122	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017123	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests / Yongtao Yu in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 112 (February 2016) . - pp 50 – 64 Titre : Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests Type de document : Article/Communication Auteurs : Yongtao Yu, Auteur ; Haiyan Guan, Auteur ; Dawei Zai, Auteur ; Zheng Ji, Auteur Année de publication : 2016 Article en page(s) : pp 50 – 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aéronef [Termes IGN] détection d'objet [Termes IGN] invariant [Termes IGN] Rotation Forest classification [Termes IGN] transformation de Hough Résumé : (auteur) This paper proposes a rotation-and-scale-invariant method for detecting airplanes from high-resolution satellite images. To improve feature representation capability, a multi-layer feature generation model is created to produce high-order feature representations for local image patches through deep learning techniques. To effectively estimate airplane centroids, a Hough forest model is trained to learn mappings from high-order patch features to the probabilities of an airplane being present at specific locations. To handle airplanes with varying orientations, patch orientation is defined and integrated into the Hough forest to augment Hough voting. The scale invariance is achieved by using a set of scale factors embedded in the Hough forest. Quantitative evaluations on the images collected from Google Earth service show that the proposed method achieves a completeness, correctness, quality, and F1-measure of 0.968, 0.972, 0.942, and 0.970, respectively, in detecting airplanes with arbitrary orientations and sizes. Comparative studies also demonstrate that the proposed method outperforms the other three existing methods in accurately and completely detecting airplanes in high-resolution remotely sensed images. Numéro de notice : A2016-139 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.04.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.04.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80313 [article]