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3D reconstruction methods based on the rational function model / C. Vincent Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 7 (July 2002)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 7 (July 2002) . - pp 705 - 714 Titre : 3D reconstruction methods based on the rational function model Type de document : Article/Communication Auteurs : C. Vincent Tao, Auteur ; Y. Hu, Auteur Année de publication : 2002 Article en page(s) : pp 705 - 714 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] correction d'image [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] modèle par fonctions rationnelles [Termes IGN] orthorectification [Termes IGN] reconstruction 3D [Termes IGN] stéréoscopie Résumé : (Auteur) The rational function model (RFM) is an alternative sensor model allowing users to perform photogrammetric processing. The RFM has been used as a replacement sensor model in some commercial photogrammetric systems due to its capability of maintaining the accuracy of the physical sensor models and its generic characteristic of supporting sensor-independent photogrammetric processing. With RFM parameters provided, end users are able to perform photogrammetric processing including ortho-rectification, 3D reconstruction, and DEM generation with an absence of the physical sensor model. In this research, we investigate two methods for RFM-based 3D reconstruction, the inverse RFM method and the forward RFM method. Detailed derivations of algorithmic procedure are described. The emphasis is placed on the comparison of these two reconstruction methods. Experimental results show that the foward RFM can achieve a better reconstruction accuracy. Finally, real Ikonos stereo pairs were employed to verify the applicability and the performance of the reconstruction method. Numéro de notice : A2002-368 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.semanticscholar.org/paper/3D-Reconstruction-methods-based-on-the-rat [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22280 [article]

Updating solutions of the rational function model using additional control information / Y. Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 7 (July 2002)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 68 n° 7 (July 2002) . - pp 715 - 723 Titre : Updating solutions of the rational function model using additional control information Type de document : Article/Communication Auteurs : Y. Hu, Auteur ; C. Vincent Tao, Auteur Année de publication : 2002 Article en page(s) : pp 715 - 723 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] capteur (télédétection) [Termes IGN] déformation d'image [Termes IGN] données localisées 3D [Termes IGN] extraction automatique [Termes IGN] mise à jour [Termes IGN] modèle par fonctions rationnelles [Termes IGN] orthorectification [Termes IGN] point d'appui [Termes IGN] terrain Résumé : (Auteur) The rational function model (RFM) is a sensor model that allows users to perform ortho-rectification and 3D feature extraction from imagery without knowledge of the physical sensor model. It is a fact that the RFM is determined by the vendor using a proprietary physical sensor model. The accuracy of the RFM solutions is dependent on the avaibility and the usage of ground control points (GCPs). In order to obtain a more accurate RFM solution, the user may be asked to supply GCPs to the data vendor. However, control information may not be available at the time of data processing or cannot be supplied due to some reasons (e.g., politics or confidentility). This paper adresses a means to update or improve the existing RFM solutions when additional GCPs are available, without knowing the physical sensor model. From a linear estimation perspective, the above issue can be tackled using a phased estimation theory . In this paper, two methods are proposed : a batch iterative least-squares (BILS) method and an incremental discrete Kalman filtering (IDKF) method. Detailed descriptions of both methods are given. The feasability of these two methods is validated and their performances are evaluated. Some results concerning the updating of Ikonos imagery are also discussed. Numéro de notice : A2002-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/2002journal/july/2002_jul_715-723. [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22281 [article]