Centre de documentation
scientifique

Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 1836 - 1847 Titre : Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery Type de document : Article/Communication Auteurs : Ju Zhang, Auteur ; Qingwu Hu, Auteur ; Jiayuan Li, Auteur ; Mingyao Ai, Auteur Année de publication : 2021 Article en page(s) : pp 1836 - 1847 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] extraction du réseau routier [Termes IGN] image à haute résolution [Termes IGN] rastérisation [Termes IGN] segmentation d'image [Termes IGN] trace GPS [Termes IGN] trace numérique [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] Wuhan (Chine) [Termes IGN] zone urbaine Résumé : (Auteur) Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work. Numéro de notice : A2021-211 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003425 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003425 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97196 [article]

4FP-structure: a robust local region feature descriptor / Jiayuan Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 12 (December 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 12 (December 2017) . - pp 813 - 826 Titre : 4FP-structure: a robust local region feature descriptor Type de document : Article/Communication Auteurs : Jiayuan Li, Auteur ; Qingwu Hu, Auteur ; Mingyao Ai, Auteur Année de publication : 2017 Article en page(s) : pp 813 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] corrélation automatique de points homologues [Termes IGN] méthode robuste [Termes IGN] points homologues [Termes IGN] transformation affine Résumé : (Auteur) Establishing reliable correspondence for images of the same scene is still challenging work due to repetitive texture and unknown distortion. In this paper, we propose a region-matching method to simultaneously filter false matches and maximize good correspondence between images, even those with irregular distortion. First, a novel region descriptor, represented by a structure formed by four feature points (4FP-Structure), is presented to simplify matching with severe deformation. Furthermore, an expansion stage based on the special 4FP-Structure is adapted to detect and select as many high location accuracy correspondences as possible under a local affine-transformation constraint. Extensive experiments on both rigid and non-rigid image datasets demonstrate that the proposed algorithm has a very high degree of correctness and significantly outperforms other state-of-the-art methods. Numéro de notice : A2017-803 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.12.813 En ligne : https://doi.org/10.14358/PERS.83.12.813 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89164 [article]

Exterior orientation revisited : a robust method based on lq -norm / Jiayuan Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 1 (January 2017) . - pp 47 - 56 Titre : Exterior orientation revisited : a robust method based on lq -norm Type de document : Article/Communication Auteurs : Jiayuan Li, Auteur ; Qingwu Hu, Auteur ; Ruofei Zhong, Auteur ; Mingyao Ai, Auteur Année de publication : 2017 Article en page(s) : pp 47 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] caméra numérique [Termes IGN] équation de Lagrange [Termes IGN] orientation du capteur [Termes IGN] orientation externe [Termes IGN] valeur aberrante Résumé : (Auteur) Camera exterior orientation is essential in many photogrammetry and computer vision applications, including 3D reconstruction, digital orthophoto map (DOM) generation, and localization. In this paper, we propose a new formulation of exterior orientation that is robust against gross errors (outliers). Different from classic optimization methods whose cost function is based on the l q -norm of residuals, we use l q -norm (0 Numéro de notice : A2017-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.1.47 En ligne : https://doi.org/10.14358/PERS.83.1.47 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84092 [article]