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Filtering airborne lidar data by modified white top-hat transform with directional edge constraints / Yong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 133 - 141 Titre : Filtering airborne lidar data by modified white top-hat transform with directional edge constraints Type de document : Article/Communication Auteurs : Yong Li, Auteur ; Bin Yong, Auteur ; Huayi Wu, Auteur Année de publication : 2014 Article en page(s) : pp 133 - 141 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] contrainte géométrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] morphologie mathématique [Termes IGN] objet géographique ponctuel [Termes IGN] programmation par contraintes [Termes IGN] sous-sol Résumé : (Auteur) A novel algorithm that employs modified white top-hat (MWTH) transform with directional edge constraints is proposed in this study to automatically extract ground points from airborne light detection and ranging (lidar) data. MWTH transform can effectively distinguish above-ground objects that are smaller than the window size and higher than the height difference threshold. Directional edge constraints significantly decrease omission errors from protruding ground features. Incorporating MWTH transform and directional edge constraints enables the simultaneous consideration of the size, height, and edge characteristics of lidar data for judging above-ground objects. Experimental results verify that the proposed algorithm exhibits promising performance and high accuracy in various complicated landscapes, even in areas with dramatic changes in elevation. The proposed algorithm has minimal omission and commission error oscillation for different test sites, thereby demonstrating its stability and reliability in a wide range of applications. Numéro de notice : A2014-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.133-141 En ligne : https://doi.org/10.14358/PERS.80.2.133-141 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33013 [article]

Multi-agent recognition system based on object based image analysis using WorldView-2 / Fatemeh Tabib Mahmoudi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 161 - 170 Titre : Multi-agent recognition system based on object based image analysis using WorldView-2 Type de document : Article/Communication Auteurs : Fatemeh Tabib Mahmoudi, Auteur ; Farhad Samadzadegan, Auteur ; Peter Reinartz, Auteur Année de publication : 2014 Article en page(s) : pp 161 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] classification à base de connaissances [Termes IGN] détection de régions [Termes IGN] image Worldview [Termes IGN] reconnaissance d'objets [Termes IGN] système multi-agents [Termes IGN] zone urbaine dense Résumé : (Auteur) In this paper, using spatial and spectral characteristics of the WorldView-2 satellite imagery, capabilities of multi-agent systems are used for solving multiple object recognition difficulties in complex urban areas. The methodology has two main steps: object based image analysis (OBIA) and multi-agent object recognition. In the first step, segmentation and multi-process object classification based on spectral, textura, and structural features are performed. Classified regions are used as an input dataset in the multi-agent system in order to modify object recognition results. According to the results from the object based image analysis process, using contextual relations and structural features, the overall accuracy and Kappa improved by 17.79 percent and 0.253, respectively. Using knowledge-based reasoning and cooperative capabilities of agents in the multi-agent system in this paper most of the remaining difficulties are decreased and values 90.95 percent and 0.876 are obtained for the overall accuracy and Kappa, respectively, of the object recognition results. Numéro de notice : A2014-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.161-170 En ligne : https://doi.org/10.14358/PERS.80.2.161-170 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33014 [article]

An automatic parameter selection procedure for pushbroom sensor models on imaging satellites / Inseong Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 171 - 178 Titre : An automatic parameter selection procedure for pushbroom sensor models on imaging satellites Type de document : Article/Communication Auteurs : Inseong Jeong, Auteur ; James Bethel, Auteur Année de publication : 2014 Article en page(s) : pp 171 - 178 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse comparative [Termes IGN] capteur en peigne [Termes IGN] capteur spatial [Termes IGN] procédure opérationnelle Résumé : (Auteur) For the rigorous, physical modeling of spaceborne, push-broom imaging sensors, over-parameterization and the resulting dependencies among sensor model parameters are a continuing issue causing instability and ambiguity during parameter estimation. Traditionally, this problem hat been tackled by a fixed subset approach or by using a priori stochastic constraints, which generally require the user's intuition or intervention but with no guarantee that an optima, solution is obtained. An efficient and automated procedure to find an optimal parameter subset, that is independent and meets accuracy requirements, has been developed and tested using six images from six representative sensors. The experimental results show a stable performance of the developed procedure which results in a quality subset by the evaluation criteria and tries to minimize the checkpoint misclosure (i.e., loocvrmse) so that the resulting subset can be considered optimum. Therefore, the proposed procedure can be beneficial to the users and sensor model developers by providing an optimal and subjective solution to the well known over-parameterization problem in satellite sensor model. Numéro de notice : A2014-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.171-178 En ligne : https://doi.org/10.14358/PERS.80.2.171-178 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33015 [article]

Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 179 - 189 Titre : Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2014 Article en page(s) : pp 179 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse combinatoire (maths) [Termes IGN] données lidar [Termes IGN] image RapidEye [Termes IGN] mine [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] séparateur à vaste marge [Termes IGN] visualisation cartographique Résumé : (Auteur) The combination of RapidEye satellite imagery and light detection and ranging (lidar) derivatives was assessed for mapping land-cover within a mountaintop coal surface mine complex in the southern coalfields of West Virginia, USA. Support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART) algorithms were used. Incorporation of the lidar-derived data increased map accuracy in comparison to using only the five imagery bands, and SVM generally produced a more accurate classification than the ensemble tree algorithms based on overall map accuracy, Kappa statistics, allocation disagreement, quantity disagreement, and McNemar's test of statistical significance. Based on measures of predictor variable importance within the ensemble tree classifiers, the normalized digital surface model (nDSM) was found to be more useful than first return intensity data for differentiating the classes Numéro de notice : A2014-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.179-189 En ligne : https://doi.org/10.14358/PERS.80.2.179-189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33016 [article]