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A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework / P.A. Graniero in International journal of geographical information science IJGIS, vol 20 n° 9 (october 2006)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 20 n° 9 (october 2006) . - pp 965 - 990 Titre : A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework Type de document : Article/Communication Auteurs : P.A. Graniero, Auteur ; V.B. Robinson, Auteur Année de publication : 2006 Article en page(s) : pp 965 - 990 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] conception orientée objet [Termes IGN] image PROBE [Termes IGN] paysage [Termes IGN] simulation 3D [Termes IGN] système multi-agents Résumé : (Auteur) Many environmental, ecological, and social problems require investigation using a mixture of landscape models, individual-based models, and some level of interaction between them. Few simulation-modelling frameworks are structured to handle both styles of model in an integrated fashion. ECO-COSM is a framework that is capable of handling complex models with both landscape and agent components. Its Probe-based architecture allows model components to have controlled access to the state of other components. The ProbeWrapper is a modification of this common design approach which allows alterations to the state retrieved from the model and is a critical component of ECO-COSM's broad modelling capability. It allows agents to apply perceptual filters or measurement errors to their observations of the landscape, or apply decision-making strategies in the face of incomplete or uncertain observations. ECO-COSM is demonstrated with a landscape model of metapopulation dynamics, an agent model of squirrel dispersal, and a coupled landscape-agent model to evaluate field-data-acquisition strategies for identifying nutrient or contaminant hotspots. Copyright Taylor & Francis Numéro de notice : A2006-420 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810600830541 En ligne : https://doi.org/10.1080/13658810600830541 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28144 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-06091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-06092	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis / R. Lawrence in Remote sensing of environment, vol 90 n° 3 (15/04/2004)  

Public [article]  inRemote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 331 - 336 Titre : Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis Type de document : Article/Communication Auteurs : R. Lawrence, Auteur ; A. Bunn, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 331 - 336 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification [Termes IGN] décomposition d'image [Termes IGN] image hyperspectrale [Termes IGN] image Ikonos [Termes IGN] image Landsat-ETM+ [Termes IGN] image PROBE [Termes IGN] précision de la classification [Termes IGN] sylviculture Résumé : (Auteur) Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the training data and (2) combining the multiple trees iteratively developed to classify the data. We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City mining district of Montana, and a series of Landsat ETM+ images from the Greater Yellowstone Ecosystem (GYE). SGB improved the overall accuracy of the IKONOS classification from 84% to 95% and the Probe-1 classification from 83% to 93%. The worst performing classes using CTA exhibited the largest increases in class accuracy using SGB. A slight decrease in overall classification accuracy resulted from the SGB analysis of the Landsat data. Numéro de notice : A2004-200 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.007 En ligne : https://doi.org/10.1016/j.rse.2004.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26727 [article]

A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) . - pp 2488 - 2499 Titre : A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery Type de document : Article/Communication Auteurs : C. Bachmann, Auteur ; M.H. Bettenhausen, Auteur ; R.A. Fusina, Auteur ; et al., Auteur Année de publication : 2003 Article en page(s) : pp 2488 - 2499 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classificateur [Termes IGN] classification [Termes IGN] image aérienne [Termes IGN] image HYMAP [Termes IGN] image hyperspectrale [Termes IGN] image PROBE [Termes IGN] littoral [Termes IGN] occupation du sol [Termes IGN] parc naturel [Termes IGN] Virginie (Etats-Unis) Résumé : (Auteur) A credit assignment approach to decision-based classifier fusion is developed and applied to the problem of land-cover classification from multiseason airborne hyperspectral imagery. For each input sample, the new method uses a smoothed estimated reliability measure (SERM) in the output domain of the classifiers. SERM requires no additional training beyond that needed to optimize the constituent classifiers in the pool, and its generalization (test) accuracy exceeds that of a number of other extant methods for classifier fusion. Hyperspectral imagery from HyMAP and PROBE2 acquired at three points in the growing season over Smith Island, VA, a barrier island in the Nature Conservancy's Virginia Coast Reserve, serves as the basis for comparing SERM with other approaches. Numéro de notice : A2003-318 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818537 En ligne : https://doi.org/10.1109/TGRS.2003.818537 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22614 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03111	RAB	Revue	Centre de documentation	En réserve L003	Disponible