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A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 18 - 29 Titre : A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape Type de document : Article/Communication Auteurs : Jason R. Parent, Auteur ; John C. Volin, Auteur ; Daniel L. Civco, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 29 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte d'occupation du sol [Termes IGN] classification automatique [Termes IGN] classification pixellaire [Termes IGN] Connecticut (Etats-Unis) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuillu [Termes IGN] forêt ripicole [Termes IGN] image multibande [Termes IGN] Pinophyta Résumé : (auteur) Information on land cover is essential for guiding land management decisions and supporting landscape-level ecological research. In recent years, airborne light detection and ranging (LiDAR) and high resolution aerial imagery have become more readily available in many areas. These data have great potential to enable the generation of land cover at a fine scale and across large areas by leveraging 3-dimensional structure and multispectral information. LiDAR and other high resolution datasets must be processed in relatively small subsets due to their large volumes; however, conventional classification techniques cannot be fully automated and thus are unlikely to be feasible options when processing large high-resolution datasets. In this paper, we propose a fully automated rule-based algorithm to develop a 1 m resolution land cover classification from LiDAR data and multispectral imagery. The algorithm we propose uses a series of pixel- and object-based rules to identify eight vegetated and non-vegetated land cover features (deciduous and coniferous tall vegetation, medium vegetation, low vegetation, water, riparian wetlands, buildings, low impervious cover). The rules leverage both structural and spectral properties including height, LiDAR return characteristics, brightness in visible and near-infrared wavelengths, and normalized difference vegetation index (NDVI). Pixel-based properties were used initially to classify each land cover class while minimizing omission error; a series of object-based tests were then used to remove errors of commission. These tests used conservative thresholds, based on diverse test areas, to help avoid over-fitting the algorithm to the test areas. The accuracy assessment of the classification results included a stratified random sample of 3198 validation points distributed across 30 1 × 1 km tiles in eastern Connecticut, USA. The sample tiles were selected in a stratified random manner from locations representing the full range of rural to urban landscapes in eastern Connecticut. The overall land cover accuracy was 93% with accuracies exceeding 90% for deciduous trees, low vegetation, water, buildings, and low impervious cover. Slight confusion occurred between coniferous and deciduous trees; major confusion occurred between water and riparian wetlands; and moderate confusion occurred between medium vegetation and other vegetation classes. The algorithm was robust for the forested suburban landscape of eastern Connecticut, which is typical for much of the northeastern U.S., and the algorithm shows promise for applications in similar landscapes with similar datasets. Further research is needed to test the applicability of the algorithm to more diverse landscapes as well as with different LiDAR and multispectral datasets. Numéro de notice : A2015-698 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78334 [article]

A neural network-based method for solving "nested hierarchy" areal interpolation problems / D. Merwin in Cartography and Geographic Information Science, vol 36 n° 4 (October 2009)  

Public [article]  inCartography and Geographic Information Science > vol 36 n° 4 (October 2009) . - pp 347 - 365 Titre : A neural network-based method for solving "nested hierarchy" areal interpolation problems Type de document : Article/Communication Auteurs : D. Merwin, Auteur ; R. Cromley, Auteur ; Daniel L. Civco, Auteur Année de publication : 2009 Article en page(s) : pp 347 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse comparative [Termes IGN] Connecticut (Etats-Unis) [Termes IGN] figuration de la densité [Termes IGN] interpolation par pondération de zones [Termes IGN] interpolation spatiale [Termes IGN] prévision [Termes IGN] recensement démographique [Termes IGN] régression linéaire [Termes IGN] réseau neuronal artificiel [Termes IGN] structure hiérarchique de données Résumé : (Auteur) This study proposes a neural network approach to solving areal interpolation scenarios, specifically the “nested hierarchy” problem. The neural network method presented adopts the approach taken by intelligent interpolation methods where ancillary spatial information is presented to assist in achieving more accurate results. For this study, the data to be estimated are total populations for census tracts and block groups in Hartford County, Connecticut. A number of neural network models are generated containing various combinations of ancillary spatial information. The neural-network-derived predictions are compared with the predicted populations derived from three existing interpolation methods: areal weighting, a dasymetric areal weighting approach using remote sensing data, and ordinary least squares (OLS) regression. For each scenario presented, the proposed neural network approach outperforms each of the existing methods. Numéro de notice : A2009-441 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article DOI : 10.1559/152304009789786335 En ligne : https://doi.org/10.1559/152304009789786335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30072 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-09041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Road extraction using SVM and image segmentation / M. Song in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 12 (December 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 12 (December 2004) . - pp 1365 - 1371 Titre : Road extraction using SVM and image segmentation Type de document : Article/Communication Auteurs : M. Song, Auteur ; Daniel L. Civco, Auteur Année de publication : 2004 Article en page(s) : pp 1365 - 1371 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] bande spectrale [Termes IGN] classification orientée objet [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction du réseau routier [Termes IGN] objet géographique linéaire [Termes IGN] pixel [Termes IGN] segment de droite [Termes IGN] segmentation d'image [Termes IGN] seuillage d'image [Termes IGN] vectorisation Résumé : (Auteur) In this paper, a unique approach for road extraction utilizing pixel spectral information for classification and image segmentation-derived object features was developed. In this approach, road extraction was performed in two steps. In the first step, support vector machine (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. For this classification, support vector machine (SVM) achieved higher accuracy than Gaussian maximum likelihood (GML). In the second step, the road group image was segmented into geometrically homogeneous objects using a region growing technique based on a similarity criterion, with higher weighting on shape factors over spectral criteria. A simple thresholding on the shape index and density features derived from these objects was performed to extract road features, which were further processed by thinning and vectorization to obtain road centerlines. The experiment showed the proposed approach worked well with images comprised by both rural and urban area features. Numéro de notice : A2004-500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.12.1365 En ligne : https://doi.org/10.14358/PERS.70.12.1365 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27017 [article]

Artificial neural networks as a method of spatial interpolation for digital elevation models / D.A. Merwin in Cartography and Geographic Information Science, vol 29 n° 2 (April 2002)  

Public [article]  inCartography and Geographic Information Science > vol 29 n° 2 (April 2002) . - pp 99 - 110 Titre : Artificial neural networks as a method of spatial interpolation for digital elevation models Type de document : Article/Communication Auteurs : D.A. Merwin, Auteur ; R.G. Cromley, Auteur ; Daniel L. Civco, Auteur Année de publication : 2002 Article en page(s) : pp 99 - 110 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] interpolation spatiale [Termes IGN] modèle numérique de surface [Termes IGN] réseau neuronal artificiel [Termes IGN] valeur efficace Résumé : (Auteur) This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance. Numéro de notice : A2002-144 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1559/152304002782053323 En ligne : https://doi.org/10.1559/152304002782053323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22059 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Using genetic learning neural networks for spatial decision making in GIS / J. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 62 n° 11 (november 1996)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 62 n° 11 (november 1996) . - pp 1287 - 1295 Titre : Using genetic learning neural networks for spatial decision making in GIS Type de document : Article/Communication Auteurs : J. Zhou, Auteur ; Daniel L. Civco, Auteur Année de publication : 1996 Article en page(s) : pp 1287 - 1295 Note générale : Bibliographie 1 page Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] aide à la décision [Termes IGN] algorithme génétique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] système expert Numéro de notice : A1996-001 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : sans En ligne : https://www.asprs.org/wp-content/uploads/pers/1996journal/nov/1996_nov_1287-1295 [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25786 [article]

Topographic normalization of Landsat Thematic Mapper digital imagery / Daniel L. Civco in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 9 (september 1989)  

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Adaptation of a hand-held radiometer for measuring upwelling radiance in the aquatic environment / C.W. Brown in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 2 (february 1989)  

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