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A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 6 (June 2018) Titre : A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data Type de document : Article/Communication Auteurs : Biao He, Auteur ; Zhang Yan, Auteur ; Yu Chen, Auteur ; Zhihui Gu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] bicyclette [Termes IGN] entropie [Termes IGN] extraction de modèle [Termes IGN] origine - destination [Termes IGN] raisonnement spatial [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. Numéro de notice : A2018-345 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7060203 Date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.10.3390/ijgi7060203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90568 [article]

Object-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 328 - 340 Titre : Object-based superresolution land-cover mapping from remotely sensed imagery Type de document : Article/Communication Auteurs : Yuehong Chen, Auteur ; Yong Ge, Auteur ; Gerard B.M. Heuvelink, Auteur ; Ru An, Auteur ; Yu Chen, Auteur Année de publication : 2018 Article en page(s) : pp 328 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification orientée objet [Termes IGN] classification pixellaire [Termes IGN] déconvolution [Termes IGN] krigeage [Termes IGN] occupation du sol [Termes IGN] programmation linéaire [Termes IGN] variogramme Résumé : (Auteur) Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon-a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification. Numéro de notice : A2018-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2747624 Date de publication en ligne : 20/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2747624 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89843 [article]