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Extracting urban road networks from high-resolution true orthoimage and Lidar / J. Youn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 2 (February 2008) . - pp 227 - 237 Titre : Extracting urban road networks from high-resolution true orthoimage and Lidar Type de document : Article/Communication Auteurs : J. Youn, Auteur ; J. Bethel, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 227 - 237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification dirigée [Termes IGN] données lidar [Termes IGN] extraction automatique [Termes IGN] lasergrammétrie [Termes IGN] orthoimage intégrale [Termes IGN] réseau routier [Termes IGN] segmentation en régions [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (Auteur) Automated or semi-automated feature extraction from remotely collected, large scale image data has been a challenging issue in digital photogrammetry for many years. In the feature extraction field, fusing different types of data to provide complementary information about the objects is becoming increasingly important. In this paper, we present a newly developed approach for the automatic extraction of urban area road networks from a true orthoimage and lidar assuming the road network to be a semi-grid pattern. The proposed approach starts from the subdivision of a study area into small regions based on homogeneity of the dominant road directions from the true orthoimage. Each region’s road candidates are selected with a proposed free passage measure. This process is called the “acupuncture” method. Features around the road candidates are used as key factors for an advanced “acupuncture method” called the region-based acupuncture method. Extracted road candidates are edited to avoid collocation with non-road features such as buildings and grass fields. In order to produce a building map for the prior step, a first-last return analysis and morphological filter are used with the lidar point cloud. A grass area thematic map is generated by supervised classification techniques from a synthetic image, which contains the three color bands from the true orthoimage and the lidar intensity value. Those non-road feature maps are used as a blocking mask for the roads. The accuracy of the result is evaluated quantitatively with respect to manually compiled road vectors, and a completeness of 80 percent and a correctness of 79 percent are obtained with the proposed algorithm on an area of 1,081,600 square meters. Copyright ASPRS Numéro de notice : A2008-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.2.227 En ligne : https://doi.org/10.14358/PERS.74.2.227 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29042 [article]

Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 2 (February 2008) . - pp 239 - 246 Titre : Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers Type de document : Article/Communication Auteurs : P. Watanachaturaporn, Auteur ; M. Arora, Auteur ; K. Varshney, Auteur Année de publication : 2008 Article en page(s) : pp 239 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification par arbre de décision [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données multisources [Termes IGN] extraction automatique [Termes IGN] Himalaya [Termes IGN] image IRS-LISS [Termes IGN] Kappa de Cohen [Termes IGN] modèle numérique de surface [Termes IGN] occupation du sol Résumé : (Auteur) Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multisource classification. An IRS–1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multisource classification is compared with several other nonparametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. Copyright ASPRS Numéro de notice : A2008-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.2.239 En ligne : https://doi.org/10.14358/PERS.74.2.239 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29043 [article]