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Multi-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 2 (February 2016) . - pp 107 - 120 Titre : Multi-criteria, graph-based road centerline vectorization using ordered weighted averaging operators Type de document : Article/Communication Auteurs : Fateme Ameri, Auteur ; Mohammad Javad Valadan Zoej, Auteur ; Mehdi Mokhtarzade, Auteur Année de publication : 2016 Article en page(s) : pp 107 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] classification dirigée [Termes IGN] extraction du réseau routier [Termes IGN] graphe [Termes IGN] image satellite [Termes IGN] pondération [Termes IGN] réseau routier [Termes IGN] théorie des graphes [Termes IGN] vectorisation Résumé : (auteur) In this paper, a novel road vectorization methodology based on image space clustering technique and weighted graph theory is presented. The proposed methodology describes a road as a set of optimized points on the centerline which should be connected by defining a number of appropriate criteria. The main contribution of this paper is to design a weighting scheme for combining a small number of road identities using Ordered Weighted Averaging (OWA) operators by defining appropriate decision strategy. In this regard, a novel geometric criterion is introduced. Result of the OWA aggregation specifies weight of each edge in the road network graph. Comparing the proposed approach with two state-of-the-art image space clustering-based road vectorization methods proves its efficiency to deal with roads with different widths, parallel roads with different distances, different types of intersections, and also noise clusters. Obtaining improved quality measures for several high-resolution images, demonstrates the successfulness of the vectorization approach. Numéro de notice : A2016-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.2.107 En ligne : http://dx.doi.org/10.14358/PERS.82.2.107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79655 [article]

Seamline determination for high resolution orthoimage mosaicking using watershed segmentation / Wang Mi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 2 (February 2016) . - pp 121 - 133 Titre : Seamline determination for high resolution orthoimage mosaicking using watershed segmentation Type de document : Article/Communication Auteurs : Wang Mi, Auteur ; Yuan Shenggu, Auteur ; Pan Jun, Auteur Année de publication : 2016 Article en page(s) : pp 121 - 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chemin le plus court, algorithme du [Termes IGN] classification pixellaire [Termes IGN] coefficient de corrélation [Termes IGN] extraction de traits caractéristiques [Termes IGN] image aérienne [Termes IGN] mosaïquage d'images [Termes IGN] orthoimage [Termes IGN] orthophotoplan numérique [Termes IGN] raccord d'images [Termes IGN] segmentation d'image [Termes IGN] zone d'intérêt Résumé : (auteur) Image mosaicking is a process during which multiple orthoimages are combined into a single seamless composite orthoimage. One of the most difficult steps in the automatic mosaicking of orthoimages is the seamline determination. This paper presents a novel algorithm that selects seamlines based on marker-based watershed segmentation. A representative seamline is extracted at the object level and the pixel level as follows. First, a watershed segmentation is performed to obtain the objects. To avoid over-segmentation, a regional adaptive marker-based watershed segmentation is proposed. Second, the object difference estimated by the correlation coefficient of each object is calculated, and the region adjacency matrix is built. Third, a technique for minimizing the maximum object cost is adopted to determine the objects through which the seamlines pass. Finally, pixel-level optimization is performed using Dijkstra's algorithm with a binary min-heap to determine the final seamlines. The experimental results on digital aerial orthoimages in different areas demonstrate the feasibility and effectiveness of the proposed method compared with other algorithms. Numéro de notice : A2016-055 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.2.121 En ligne : https://doi.org/10.14358/PERS.82.2.121 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79656 [article]

A region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 2 (February 2016) . - pp 149 - 159 Titre : A region-line primitive association framework for object-based remote sensing image analysis Type de document : Article/Communication Auteurs : Wang Min, Auteur ; Wang Jie, Auteur Année de publication : 2016 Article en page(s) : pp 149 - 159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] primitive géométrique [Termes IGN] primitive topologique [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] zone d'intérêt Résumé : (auteur) In this study, we propose a novel region-line primitive association framework (RLPAF) for OBIA. In this framework, segments (region primitive) and straight lines (line primitive) are obtained by image segmentation and straight line detection, respectively, before their corresponding intra-primitive features are extracted. An association model is built on inter-primitive topology and direction relationships. Several region-line collaborative features are also derived. Image analysis is then performed based on both region and line primitives. The advantage of RLPAF is the collaborative utilization of complementary information between regions and lines throughout the entire OBIA process: from image segmentation, to feature extraction, and finally, object recognition. To validate this framework, RLPAF is applied on road network extraction from high spatial resolution (HSR) remote sensing images. Experiments show that the proposed framework and methods refine primitive shape and spatial relationship analyses, as well as obtain higher method accuracy, than OBIAs based on only regions. Numéro de notice : A2016-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.2.149 En ligne : http://dx.doi.org/10.14358/PERS.82.2.149 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79657 [article]