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Uncertainty and confidence in land cover classification using a hybrid classifier approach / W. Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 8 (August 2004)
[article]
Titre : Uncertainty and confidence in land cover classification using a hybrid classifier approach Type de document : Article/Communication Auteurs : W. Liu, Auteur ; Sucharita Gopal, Auteur ; Curtis E. Woodcock, Auteur Année de publication : 2004 Article en page(s) : pp 963 - 971 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Amérique du nord
[Termes IGN] classification hybride
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] image NOAA-AVHRR
[Termes IGN] incertitude des données
[Termes IGN] occupation du solRésumé : (Auteur) Traditional methods of land cover classification and mapping are limited in providing spatial data on the uncertainty of map labels. In this paper, we present a hybrid classifier approach using Decision Tree (DT) and ARTMAP neural network to providing confidence or uncertainty information via majority voting and other rules. The hybrid classifier is tested with AVHRR data to mapping land cover of North America. The two classifiers (DT and ARTMAP) tend to make predictive errors in different contexts. They show 68% agreement in classifying land cover of North America. A set of rules is developed to assign class labels for pixels where the two classifiers disagree. Levels of confidence in the hybrid classification derived from their individual voting (ARTmAP) and probability (DT) are used to assign confidence. The approach outlined in this paper produces two products a hybrid classification map as well as a confidence map based on the two classification schemes. The hybrid approach seems suitable to tackle a variety of classification problems in remote sensing and may ultimately aid map users in making more informed decisions. Numéro de notice : A2004-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.8.963 En ligne : https://doi.org/10.14358/PERS.70.8.963 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26836
in Photogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 8 (August 2004) . - pp 963 - 971[article]Mapping regional land cover with MODIS data for biological conservation: examples from the greater Yellowstone ecosystem, USA and PARA state, Brazil / K.J. Wessels in Remote sensing of environment, vol 92 n° 1 (15 July 2004)
[article]
Titre : Mapping regional land cover with MODIS data for biological conservation: examples from the greater Yellowstone ecosystem, USA and PARA state, Brazil Type de document : Article/Communication Auteurs : K.J. Wessels, Auteur ; R.S. de Fries, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 67 - 83 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biodiversité
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] déboisement
[Termes IGN] fractionnement
[Termes IGN] habitat animal
[Termes IGN] image Landsat-TM
[Termes IGN] image Terra-MODIS
[Termes IGN] limite de résolution géométrique
[Termes IGN] occupation du sol
[Termes IGN] Para (Brésil)
[Termes IGN] parc naturel nationalRésumé : (Auteur) The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem-GYE, USA and the Pard State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pard State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them. Numéro de notice : A2004-299 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.05.002 En ligne : https://doi.org/10.1016/j.rse.2004.05.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26826
in Remote sensing of environment > vol 92 n° 1 (15 July 2004) . - pp 67 - 83[article]Mapping residential density patterns using multi- temporal Landsat data and decision-tree classifier / S. Mccauley in International Journal of Remote Sensing IJRS, vol 25 n° 6 (March 2004)
[article]
Titre : Mapping residential density patterns using multi- temporal Landsat data and decision-tree classifier Type de document : Article/Communication Auteurs : S. Mccauley, Auteur ; S.J. Goetz, Auteur Année de publication : 2004 Article en page(s) : pp 1077 - 1094 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse discriminante
[Termes IGN] classification par arbre de décision
[Termes IGN] densité de population
[Termes IGN] image Landsat-TM
[Termes IGN] population urbaine
[Termes IGN] utilisation du solRésumé : (Auteur) We examined the utility of Landsat Thematic Mapper (TM) imagery for mapping residential land use in Montgomery County, Maryland, USA. The study area was chosen partly because of the availability of a unique parcel-level database of land use attributes and an associated digital map of parcel boundaries. These data were used to develop a series of land use classifications from a combination of leaf-on and leaf-off TM image derivatives and an algorithm based on 'decision tree' theory. Results suggest potential utility of the approach, particularly to state and local governments for land use mapping and planning applications, but greater accuracies are needed for broad practical application. In general, it was possible to discriminate different densities of residential development, and to separate these from commercial/industrial and agricultural areas. Difficulties arose in the discrimination of low-density residential areas due to the range of land cover types within this specific land use, and their associated spatial variability. The greater classification errors associated with these low-density developed areas were not unexpected. We found that these errors could be mitigated somewhat with techniques that consider the mode of training data selection and by incorporation of methods that account for the presence and amount of impervious surfaces (e.g. pavement and rooftops). Numéro de notice : A2004-085 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000115102 En ligne : https://doi.org/10.1080/0143116031000115102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26612
in International Journal of Remote Sensing IJRS > vol 25 n° 6 (March 2004) . - pp 1077 - 1094[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-04061 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data / Efosa Gbenga Adagbasa in Plos one, vol 17 n° 1 (January 2022)
[article]
Titre : Mapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data Type de document : Article/Communication Auteurs : Efosa Gbenga Adagbasa, Auteur ; Geofrey Mukwada, Auteur Année de publication : 2002 Article en page(s) : n° e0256672 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] composition floristique
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-ASTER
[Termes IGN] montagne
[Termes IGN] prairieRésumé : (auteur) Vegetation species succession and composition are significant factors determining the rate of ecosystem biodiversity recovery after being disturbed and subsequently vital for sustainable and effective natural resource management and biodiversity. The succession and composition of grasslands ecosystems worldwide have significantly been affected by accelerated environmental changes due to natural and anthropogenic activities. Therefore, understanding spatial data on the succession of grassland vegetation species and communities through mapping and monitoring is essential to gain knowledge on the ecosystem and other ecosystem services. This study used a random forest machine learning classifier on the Google Earth Engine platform to classify grass vegetation species with Landsat 7 ETM+ and ASTER multispectral imager (MI) data resampled with the current Sentinel-2 MSI data to map and estimate the changes in vegetation species succession. The results indicate that ASTER MI has the least accuracy of 72%, Landsat 7 ETM+ 84%, and Sentinel-2 had the highest of 87%. The result also shows that other species had replaced four dominant grass species totaling about 49 km2 throughout the study. Numéro de notice : A2022-310 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1371/journal.pone.0256672 Date de publication en ligne : 26/01/2022 En ligne : http://dx.doi.org/10.1371/journal.pone.0256672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100406
in Plos one > vol 17 n° 1 (January 2022) . - n° e0256672[article]A dynamic decision tree structure supporting urban road network automated generalization / W. Peng in Cartographic journal (the), vol 33 n° 1 (June 1996)
[article]
Titre : A dynamic decision tree structure supporting urban road network automated generalization Type de document : Article/Communication Auteurs : W. Peng, Auteur ; Jean-Claude Müller, Auteur Année de publication : 1996 Article en page(s) : pp 5 - 10 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aide à la décision
[Termes IGN] arbre de décision
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification orientée objet
[Termes IGN] classification par arbre de décision
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] objet géographique linéaire
[Termes IGN] représentation cartographique
[Termes IGN] réseau routier
[Termes IGN] structure hiérarchique de données
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Generalization is a complex task which requires a good understanding of the geometrical and semantic aspect of the map. Reasoning about spatial relationship, comparing alternative solutions, and contextual thinking are all important activities required for generalization decision-making. Such activities are not easy to simuIate in a computer without sufficient information and the support of good data structure and adequate reasoning mechanisms. This paper introduces a dynamic decision tree structure in an attempt to partly circumvent the problem of urban road network generalization through the use of object classification and aggregation hierarchies, topological data structure, decision rules, and Al technology. Apart from the construction process : it also discusses the reasoning process for decision-making, and provides two test results with some discussion on the benefits and short-comings, of the proposed approach. Numéro de notice : A1996-042 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/caj.1996.33.1.5 En ligne : https://doi.org/10.1179/caj.1996.33.1.5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25826
in Cartographic journal (the) > vol 33 n° 1 (June 1996) . - pp 5 - 10[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 030-96011 RAB Revue Centre de documentation En réserve L003 Disponible Segmentation de photographies aériennes numérisées : délimitation globale de la végétation et extraction d’informations structurelles / Rémi Jayer (1995)PermalinkA comparison of supervised maximum likelihood and decision tree classification for crop cover estimation from multitemporal Landsat MSS data / A.S. Belward in International Journal of Remote Sensing IJRS, vol 8 n° 2 (February 1987)Permalink