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Auteur C. Unsalan |
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Urban area and building detection using SIFT: Keypoints and Graph Theory / B. Simarcek in IEEE Transactions on geoscience and remote sensing, vol 47 n° 4 (April 2009)
[article]
Titre : Urban area and building detection using SIFT: Keypoints and Graph Theory Type de document : Article/Communication Auteurs : B. Simarcek, Auteur ; C. Unsalan, Auteur Année de publication : 2009 Article en page(s) : pp 1156 - 1167 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de graphes
[Termes IGN] détection du bâti
[Termes IGN] graphe
[Termes IGN] image à résolution métrique
[Termes IGN] image Ikonos
[Termes IGN] image panchromatique
[Termes IGN] milieu urbain
[Termes IGN] SIFT (algorithme)
[Termes IGN] sommet (mathématique)
[Termes IGN] zone urbaineRésumé : (Auteur) Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings. Copyright IEEE Numéro de notice : A2009-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2008440 En ligne : https://doi.org/10.1109/TGRS.2008.2008440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29714
in IEEE Transactions on geoscience and remote sensing > vol 47 n° 4 (April 2009) . - pp 1156 - 1167[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-09041 RAB Revue Centre de documentation En réserve L003 Disponible Measuring land development in urban regions using graph theoretical and conditional statistical features / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
[article]
Titre : Measuring land development in urban regions using graph theoretical and conditional statistical features Type de document : Article/Communication Auteurs : C. Unsalan, Auteur Année de publication : 2007 Article en page(s) : pp 3989 - 3999 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de graphes
[Termes IGN] extraction automatique
[Termes IGN] graphe
[Termes IGN] image à résolution métrique
[Termes IGN] image Ikonos
[Termes IGN] image panchromatique
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] segment de droite
[Termes IGN] surveillance de l'urbanisationRésumé : (Auteur) Inferring land use from satellite images is extensively studied by the remote sensing and pattern recognition communities. In previous studies, the focus was on classifying large regions due to the resolution of available satellite images. Nowadays, very high-resolution satellite imagery (Ikonos and Quickbird) allows researchers to focus on more complex land-use problems such as monitoring development in urban regions. Solutions to these complex problems may improve the life standards of city residents. To this end, we focus on automatically monitoring construction zones using their very high-resolution panchromatic satellite images through time. To monitor land development, we obtain sequential images of a selected region. Then, we extract features from each image in the sequence. Comparing values of these features, we expect to measure the degree of land development through time. In a similar study, we introduced graph theoretical measures over Ikonos imagery to measure organization in a given satellite image. This paper is an extension of our previous work with more powerful new features. Here, we first introduce a novel method to extract straight line segments using a least squares ellipse fitting. Then, we introduce four new graph theoretical features. More importantly, we introduce a novel method to embed the spatial information in gray-level co-occurrence matrix statistical features to measure land development. Finally, we test all our existing and new features to measure land development in 19 different urban construction zones. Our test set consists of Ikonos satellite images of these regions captured in separate times. Copyright IEEE Numéro de notice : A2007-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.897446 En ligne : https://doi.org/10.1109/TGRS.2007.897446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28949
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3989 - 3999[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Classifying land development in high-resolution panchromatic satellite images using straight-line statistics / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)
[article]
Titre : Classifying land development in high-resolution panchromatic satellite images using straight-line statistics Type de document : Article/Communication Auteurs : C. Unsalan, Auteur ; K.L. Boyer, Auteur Année de publication : 2004 Article en page(s) : pp 907 - 919 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] classificateur non paramétrique
[Termes IGN] classificateur paramétrique
[Termes IGN] détection de contours
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] méthode robuste
[Termes IGN] objet géographique linéaire
[Termes IGN] périphérie urbaine
[Termes IGN] zone rurale
[Termes IGN] zone urbaineRésumé : (Auteur) We introduce a set of measures based on straight lines to assess land development levels in high-resolution (1 m) panchromatic satellite images. Most urban areas locally (such as in a 400 x 400 M2 area) exhibit a preponderance of straight-line features, generally appearing in fairly simple quasi-periodic organizations. Wilderness and rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent more computationally intensive analyses. Statistical measures based on straight lines guide the analysis. We base these measures on length, contrast, orientation, periodicity, and location. On these, we trained and tested parametric and nonparametric classifiers. These tests were for a two-class problem (urban versus rural). However, because our ultimate goal is to extract residential regions, we then extended these ideas to address the detection of suburban regions. To do so, some use of spatial coherence is required; suburban regions are especially difficult to detect. Therefore, we introduce a decision system to perform suburban region classification via an overlapping voting method for consensus discovery. Our data were taken from regions all around the world, which underscores the robustness of our approach. Based on extensive testing, we can report very promising results in distinguishing developed areas. Numéro de notice : A2004-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818835 En ligne : https://doi.org/10.1109/TGRS.2003.818835 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26715
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 4 (April 2004) . - pp 907 - 919[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04041 RAB Revue Centre de documentation En réserve L003 Disponible