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A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 2994 - 3006 Titre : A point cloud feature regularization method by fusing judge criterion of field force Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Qing Liu, Auteur ; Kegen Yu, Auteur Année de publication : 2020 Article en page(s) : pp 2994 - 3006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse vectorielle [Termes IGN] arbre BSP [Termes IGN] détection de contours [Termes IGN] échantillonnage [Termes IGN] extraction de traits caractéristiques [Termes IGN] matrice de covariance [Termes IGN] modèle numérique de surface [Termes IGN] modélisation du bâti [Termes IGN] niveau de gris (image) [Termes IGN] plus proche voisin, algorithme du [Termes IGN] reconstruction d'objet [Termes IGN] semis de points [Termes IGN] spline cubique [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] Wuhan (Chine) Résumé : (auteur) Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved $k$ - $d$ tree method is used to search the $k$ neighbors of sampling point. Then, the sampling point and its $k$ neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology. Numéro de notice : A2020-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946326 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2946326 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94968 [article]

A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)  

Public [article]  inAdvances in space research > vol 64 n°1 (1 July 2019) . - pp 79 - 87 Titre : A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery Type de document : Article/Communication Auteurs : Tingting Wu, Auteur ; Ling Han, Auteur ; Qing Liu, Auteur Année de publication : 2019 Article en page(s) : pp 79 - 87 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse fractale [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] détection des nuages [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-OLI [Termes IGN] neige [Termes IGN] réflectance [Termes IGN] seuillage d'image [Termes IGN] signature spectrale Résumé : (auteur) The separation of clouds from snow is fundamentally very challenging because of their similar spectral signature. A new algorithm was proposed to detect clouds from snow in Landsat 8 imagery. Taking the Hetian District region, where there is frequent cloud and snow cover, in northwestern China as one of the typical case areas. The typical case is presented in detail to illustrate the approach produces and results. A band math method for cloud and snow discrimination index (CSDI) was firstly conducted in this paper, fractal digital number-frequency (DN-N) algorithm and hotspot analyses were applied to determine the threshold of the CSDI and eliminate false anomalies. The results showed that an overall accuracy exceeding 95% in areas with very bright land surfaces, which indicate that this algorithm is effective for detecting clouds in specific situations where the ground objects have some reflectance characteristics similar to cloud. Numéro de notice : A2019-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.03.014 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1016/j.asr.2019.03.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93512 [article]