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Detection and vectorization of roads from lidar data / S. Clode in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 5 (May 2007)
[article]
Titre : Detection and vectorization of roads from lidar data Type de document : Article/Communication Auteurs : S. Clode, Auteur ; Franz Rottensteiner, Auteur ; P. Kootsookos, Auteur ; E. Zelniker, Auteur Année de publication : 2007 Article en page(s) : pp 517 - 535 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Australie
[Termes IGN] axe médian
[Termes IGN] convolution (signal)
[Termes IGN] détection automatique
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] extraction du réseau routier
[Termes IGN] lasergrammétrie
[Termes IGN] milieu urbain
[Termes IGN] réseau routier
[Termes IGN] semis de points
[Termes IGN] vectorisationRésumé : (Auteur) A method for the automatic detection and vectorization of roads from lidar data is presented. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogeneous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk (PCD) with the image to provide three separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to two urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75 and 0.80 for two different data sets. Copyright ASPRS Numéro de notice : A2007-244 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.5.517 En ligne : https://doi.org/10.14358/PERS.73.5.517 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28607
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 5 (May 2007) . - pp 517 - 535[article]Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps / Matthieu Molinier in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)
[article]
Titre : Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps Type de document : Article/Communication Auteurs : Matthieu Molinier, Auteur ; Jorma Laaksonen, Auteur ; Tuomas Häme, Auteur Année de publication : 2007 Article en page(s) : pp 861 - 874 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'images
[Termes IGN] carte de Kohonen
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] image à très haute résolution
[Termes IGN] image optique
[Termes IGN] image satellite
[Termes IGN] recherche d'informationRésumé : (Auteur) The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites. Copyright IEEE Numéro de notice : A2007-220 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.890580 En ligne : https://doi.org/10.1109/TGRS.2006.890580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28583
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 861 - 874[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07041 RAB Revue Centre de documentation En réserve L003 Disponible Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)
[article]
Titre : Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data Type de document : Article/Communication Auteurs : S. Lu, Auteur ; K. Oki, Auteur Année de publication : 2007 Article en page(s) : pp 963 - 984 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] analyse comparative
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classification
[Termes IGN] surface cultivée
[Termes IGN] Tokyo (Japon)
[Termes IGN] utilisation du solRésumé : (Auteur) Airborne hyperspectral remote sensing was applied to agricultural land in the Miura Peninsula, near the metropolis of Tokyo in Japan. The study area is characterized by complicated land use patches, which is the general characteristic of most agricultural lands in Japan. Several feature extraction/classification methods were examined in classifying the land use and plant species. The results showed that decision boundary feature extraction (DBFE) was better than principal component analysis (PCA) as the feature extraction method. Moreover, the pre-classification process using NDVI that separates the whole study area into vegetated area and non-vegetated areas also improved the classification accuracy. After the pre-procedures, the land use and plant species were finally mapped by maximum likelihood classification (MLC) or extraction and classification of homogeneous objects (ECHO). The best kappa (overall accuracy) of classification was 0.914 (92.4%) and 0.924 (93.3%) for MLC and ECHO, respectively. The best accuracies of each category for the image were 79.5% to 100% for plant species (watermelon, pumpkin, marigold, grass and tree), 88.7% to 100% for soil types, 97.8% for concrete, and 99.4% for vinyl-mulches. Although, built-up area has low estimation accuracy, this did not affect the overall classification accuracy because it covers only a very small area. Copyright Taylor & Francis Numéro de notice : A2007-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600771561 En ligne : https://doi.org/10.1080/01431160600771561 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28462
in International Journal of Remote Sensing IJRS > vol 28 n°5-6 (March 2007) . - pp 963 - 984[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-07031 RAB Revue Centre de documentation En réserve L003 Disponible Feature extractions for small sample size classification problem / B.C. Kuo in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
[article]
Titre : Feature extractions for small sample size classification problem Type de document : Article/Communication Auteurs : B.C. Kuo, Auteur ; K.Y. Chang, Auteur Année de publication : 2007 Article en page(s) : pp 756 - 764 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] classification dirigée
[Termes IGN] décomposition du pixel
[Termes IGN] détection de contours
[Termes IGN] reconnaissance de formes
[Termes IGN] valeur propreRésumé : (Auteur) Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices. Numéro de notice : A2007-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.885074 En ligne : https://doi.org/10.1109/TGRS.2006.885074 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28453
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 756 - 764[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07031 RAB Revue Centre de documentation En réserve L003 Disponible Oil spill detection in Radarsat and Envisat SAR images / A.H. Solberg in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
[article]
Titre : Oil spill detection in Radarsat and Envisat SAR images Type de document : Article/Communication Auteurs : A.H. Solberg, Auteur ; C. Brekke, Auteur ; P.O. Husoy, Auteur Année de publication : 2007 Article en page(s) : pp 746 - 755 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification dirigée
[Termes IGN] détection automatique
[Termes IGN] détection de contours
[Termes IGN] hydrocarbure
[Termes IGN] image Envisat-ASAR
[Termes IGN] image radar
[Termes IGN] image Radarsat
[Termes IGN] marée noire
[Termes IGN] pollution des mersRésumé : (Auteur) We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected. Copyright IEEE Numéro de notice : A2007-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.887019 En ligne : https://doi.org/10.1109/TGRS.2006.887019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28452
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 746 - 755[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07031 RAB Revue Centre de documentation En réserve L003 Disponible Open-source software-operated CMOS camera for real-time mapping / Hervé Gontran in Revue Française de Photogrammétrie et de Télédétection, n° 185 (Mars 2007)PermalinkSemi-automatic approach toward mapping of flat-roofed buildings within a non-stereoscopic environment / Y. Avrahami in Photogrammetric record, vol 22 n° 117 (March - May 2007)Permalink2D building change detection from high resolution aerial images and correlation digital surface models / Nicolas Champion (2007)PermalinkPermalinkAutomatic extraction and classification of vegetation areas from high resolution images in urban areas / Corina Iovan (2007)PermalinkExtraction 3D de marquages routiers à partir d'images aériennes multi-vues et quelques applications / Olivier Tournaire (2007)PermalinkExtraction of landcover themes out of aerial orthoimages in mountainous areas using external information / Arnaud Le Bris (2007)PermalinkImage Analysis and Recognition, 4th International Conference, ICIAR 2007, Montreal, Canada, August 2007 / Mohamed Kamel (2007)PermalinkPermalinkPermalink