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The “M” in digital elevation models / XiaoHang Liu in Cartography and Geographic Information Science, Vol 42 n° 3 (July 2015)  

Public [article]  inCartography and Geographic Information Science > Vol 42 n° 3 (July 2015) . - pp 235 - 243 Titre : The “M” in digital elevation models Type de document : Article/Communication Auteurs : XiaoHang Liu, Auteur ; Hai Hu, Auteur ; Peng Hu, Auteur Année de publication : 2015 Article en page(s) : pp 235 - 243 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] généralisation sémantique [Termes IGN] isomorphisme [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D Résumé : (auteur) The “M” in digital elevation models (DEM) stands for model, which literally means “a schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics.” A DEM fulfills the requirement of “a schematic description” of terrain. However, how to make it account for the “known or inferred properties” warrants further scrutiny. This article outlines three properties of terrain and examines their four implications to DEM generation. The three properties are as follows: (1) each terrain point has a single, fixed elevation; (2) terrain points have an order and sequence that is determined by their elevations; and (3) terrain has skeletons. The four implications to DEM generation methods are as follows: (1) a method must be a bijection; (2) a method must be an isomorphism in order to preserve elevation sequence; (3) a method must guarantee that the vertical error at any point, not just checkpoints, is acceptable in order to assure the vertical accuracy of a DEM; and (4) a method must involve generalization if terrain skeletons are to be preserved. These implications are discussed in the context of light detection and ranging-derived DEMs. Generalization is highlighted as the top priority for future research. Numéro de notice : A2015-241 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2014.993711 En ligne : https://doi.org/10.1080/15230406.2014.993711 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76239 [article]

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032-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spatial metrics and image texture for mapping urban land use / Martin Herold in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 991 - 1001 Titre : Spatial metrics and image texture for mapping urban land use Type de document : Article/Communication Auteurs : Martin Herold, Auteur ; XiaoHang Liu, Auteur ; K.C. Clarke, Auteur Année de publication : 2003 Article en page(s) : pp 991 - 1001 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte d'occupation du sol [Termes IGN] classification orientée objet [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] milieu urbain [Termes IGN] objet géographique [Termes IGN] occupation du sol [Termes IGN] photographie aérienne [Termes IGN] utilisation du sol Résumé : (Auteur) The arrival of new-generation, high -spatial-resolution satellite imagery (e.g., Ikonos) has opened up new opportunities for detailed mapping and analysis of urban land use. Drawing on the traditional approach used in aerial photointerpretation, this study investigates an "object-oriented" method to classify : large urban area into detailed land-use categories. Spatial metrics and texture measures are used to describe the spatial characteristics of land-cover objects within each land-use region as derived from interpreted aerial photographs. In assessing how land-use categories vary in their spatial configuration, spatial metrics were found to provide the most important information for differentiating urban land uses. A detailed land-use map with nine categories was derived for the Santa Barbara South Coast Region area. Results from our work suggest that the region-based method exploiting spatial metrics and texture measurements is a potential new avenue to extract detailed urban land-use information from high resolution satellite imagery. Numéro de notice : A2003-229 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.991 En ligne : https://doi.org/10.14358/PERS.69.9.991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22524 [article]

Integration of classification methods for improvement of land-cover map accuracy / XiaoHang Liu in ISPRS Journal of photogrammetry and remote sensing, vol 56 n° 4 (July - August 2002)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 56 n° 4 (July - August 2002) . - pp 257 - 268 Titre : Integration of classification methods for improvement of land-cover map accuracy Type de document : Article/Communication Auteurs : XiaoHang Liu, Auteur ; Andrew K. Skidmore, Auteur ; H.V. Oosten, Auteur Année de publication : 2002 Article en page(s) : pp 257 - 268 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] carte d'occupation du sol [Termes IGN] classification à base de connaissances [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] occupation du sol Résumé : (Auteur) Classifiers, which are used to recognize patterns in remotely sensing images, have complementary capabilities. This study tested whether integrating the results from individual classifiers improves classification accuracy. Two integrated approaches were undertaken. One approach used a consensus builder (CS13) to adjust classification output in the case of disagreement in classification between maximum likelihood classifier (MLC), expert system classifier (ESC) and neural network classifier (NNC). If the output classes for each individual pixel differed, the producer accuracies for each class were compared and the class with the highest producer accuracy was assigned to the pixel. The consensus builder approach resulted in a classification with a slightly lower accuracy (72%) when compared with the neural network classifier (74%), but it did significantly better than the maximum likelihood (62%) and expert system (59%) classifiers. The second approach integrated a rulebased expert system classifier and a neural network classifier. The output of the expert system classifier was used as one additional new input layer of the neural network classifier. A postprocessing using the producer accuracies and some additional expert rules was applied to improve the output of the integrated classifier. This is a relatively new approach in the field of image processing. This second approach produced the highest overall accuracy (80%). Thus, incorporating correct, complete and relevant expert knowledge in a neural network classifier leads to higher classification accuracy. Copyright ISPRS Numéro de notice : A2002-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/S0924-2716(02)00061-8 En ligne : https://doi.org/10.1016/S0924-2716(02)00061-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22083 [article]

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