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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]

Semiautomated extraction of street light poles from mobile LiDAR point-clouds / Yongtao Yu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1374 - 1386 Titre : Semiautomated extraction of street light poles from mobile LiDAR point-clouds Type de document : Article/Communication Auteurs : Yongtao Yu, Auteur ; Jonathan Li, Auteur ; Haiyan Guan, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1374 - 1386 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction du réseau routier [Termes IGN] extraction semi-automatique [Termes IGN] instrumentation Riegl [Termes IGN] lidar mobile [Termes IGN] mobilier urbain [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) This paper proposes a novel algorithm for extracting street light poles from vehicleborne mobile light detection and ranging (LiDAR) point-clouds. First, the algorithm rapidly detects curb-lines and segments a point-cloud into road and nonroad surface points based on trajectory data recorded by the integrated position and orientation system onboard the vehicle. Second, the algorithm accurately extracts street light poles from the segmented nonroad surface points using a novel pairwise 3-D shape context. The proposed algorithm is tested on a set of point-clouds acquired by a RIEGL VMX-450 mobile LiDAR system. The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light poles from huge volumes of mobile LiDAR point-clouds. Numéro de notice : A2015-140 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2338915 Date de publication en ligne : 05/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2338915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75807 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Using mobile laser scanning data for automated extraction of road markings / Haiyan Guan in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 93 - 107 Titre : Using mobile laser scanning data for automated extraction of road markings Type de document : Article/Communication Auteurs : Haiyan Guan, Auteur ; Jonathan Li, Auteur ; Yongtao Yu, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 93 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] morphologie mathématique [Termes IGN] semis de points [Termes IGN] seuillage de points [Termes IGN] signalisation routière Résumé : (Auteur) A mobile laser scanning (MLS) system allows direct collection of accurate 3D point information in unprecedented detail at highway speeds and at less than traditional survey costs, which serves the fast growing demands of transportation-related road surveying including road surface geometry and road environment. As one type of road feature in traffic management systems, road markings on paved roadways have important functions in providing guidance and information to drivers and pedestrians. This paper presents a stepwise procedure to recognize road markings from MLS point clouds. To improve computational efficiency, we first propose a curb-based method for road surface extraction. This method first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation-difference thresholds. Next, points belonging to the extracted road surface are interpolated into a geo-referenced intensity image using an extended inverse-distance-weighted (IDW) approach. Finally, we dynamically segment the geo-referenced intensity image into road-marking candidates with multiple thresholds that correspond to different ranges determined by point-density appropriate normality. A morphological closing operation with a linear structuring element is finally used to refine the road-marking candidates by removing noise and improving completeness. This road-marking extraction algorithm is comprehensively discussed in the analysis of parameter sensitivity and overall performance. An experimental study performed on a set of road markings with ground-truth shows that the proposed algorithm provides a promising solution to the road-marking extraction from MLS data. Numéro de notice : A2014-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32920 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014011	RAB	Revue	Centre de documentation	En réserve L003	Disponible