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classification par forêts d'arbres décisionnelsSynonyme(s)Random forests classification forêts aléatoiresVoir aussi |
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Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
[article]
Titre : Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas Type de document : Article/Communication Auteurs : Alexandre Hervieu , Auteur ; Arnaud Le Bris , Auteur ; Clément Mallet , Auteur Année de publication : 2016 Projets : HYEP / Weber, Christiane Article en page(s) : pp 457 - 464 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] occupation du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] zone urbaineRésumé : (auteur) An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data,usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification. Numéro de notice : A2016-826 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-3-457-2016 Date de publication en ligne : 06/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-3-457-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82697
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-3 (July 2016) . - pp 457 - 464[article]Documents numériques
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Fusion of hyperspectral and VHR ... - pdf éditeurAdobe Acrobat PDF An assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
[article]
Titre : An assessment of algorithmic parameters affecting image classification accuracy by random forests Type de document : Article/Communication Auteurs : Dee Shi, Auteur ; Xiaojun Yang, Auteur Année de publication : 2016 Article en page(s) : pp 407 - 417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] impact sur les données
[Termes IGN] occupation du sol
[Termes IGN] précision de la classificationRésumé : (Auteur) Random forests as a promising ensemble learning algorithm have been increasingly used for remote sensor image classification, and are found to perform identical or better than some popular classifiers. With only two algorithmic parameters, they are relatively easier to implement. Existing literature suggests that the performance of random forests is insensitive to changing algorithmic parameters. However, this was largely based on the classifier's accuracy that does not necessarily represent the resulting thematic map accuracy. The current study extends beyond the classifier's accuracy assessment and investigate how the algorithmic parameters could affect the resulting thematic map accuracy by random forests. A set of random forest models with different parameter settings was carefully constructed and then used to classify a satellite image into multiple land cover categories. Both the classifier's accuracy and the map accuracy were assessed. The results reveal that these parameters can affect the map accuracy up to 9 ∼16 percent for some classes, although their impact on the classifier's accuracy was quite limited. A careful parameterization prioritizing thematic map accuracy can help improve the performance of random forests in image classification, especially for spectrally complex land cover classes. These findings can help establish practical guidance on the use of random forests in the remote sensing community. Numéro de notice : A2016-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.407 En ligne : http://dx.doi.org/10.14358/PERS.82.6.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81345
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 407 - 417[article]Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
[article]
Titre : Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données topographiques
[Termes IGN] Etats-Unis
[Termes IGN] inventaire
[Termes IGN] marais salant
[Termes IGN] modèle numérique de terrain
[Termes IGN] prédiction
[Termes IGN] surveillance écologique
[Termes IGN] Virgine OccidentaleRésumé : (Auteur) The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (RF) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (NWI) dataset and were tested using a separate random subset from the NWI and a database of wetlands not found in the NWI provided by the West Virginia Division of Natural Resources (WVDNR). The models produced area under the curve (AUC) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different AUC values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored DEM-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-NWI wetlands were mapped with an AUC value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the NWI . Numéro de notice : A2016-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.437 En ligne : http://dx.doi.org/10.14358/PERS.82.6.437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81348
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 437 - 447[article]Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
[article]
Titre : Approximating prediction uncertainty for random forest regression models Type de document : Article/Communication Auteurs : John W. Coulston, Auteur ; Christine E. Blinn, Auteur ; Valerie A. Thomas, Auteur ; Randolph H. Wynne, Auteur Année de publication : 2016 Article en page(s) : pp 189 - 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] incertitude des données
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de régression
[Termes IGN] prédiction
[Termes IGN] variableRésumé : (auteur) Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models. Numéro de notice : A2016-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.3.189 En ligne : https://doi.org/10.14358/PERS.82.3.189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80506
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 189 - 197[article]Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana / Ibrahim Fayad in Remote sensing, vol 8 n° 3 (March 2016)
[article]
Titre : Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Nicolas Baghdadi, Auteur ; Jean-Stéphane Bailly, Auteur ; Nicolas Barbier, Auteur ; Valéry Gond, Auteur ; Bruno Hérault, Auteur ; Mahmoud El-Hajj, Auteur ; Frédéric Fabre, Auteur ; José Perrin, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] Guyane (département français)
[Termes IGN] hauteur des arbres
[Termes IGN] krigeage
[Termes IGN] régressionRésumé : (auteur) LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. Numéro de notice : A2016--121 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs8030240 En ligne : http://doi.org/10.3390/rs8030240 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84818
in Remote sensing > vol 8 n° 3 (March 2016)[article]Large-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)PermalinkAn assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)PermalinkApplication des techniques de photogrammétrie par drone à la caractérisation des ressources forestières / Jonathan Lisein (2016)PermalinkApport de la télédétection radar satellitaire pour la cartographie de la forêt des Landes / Yousra Hamrouni (2016)PermalinkEstimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data / Hooman Latifi in Forestry, an international journal of forest research, vol 89 n° 1 (January 2016)PermalinkForêts aléatoires pour la détection des feux tricolores à partir de profils de vitesse GPS / Yann Méneroux (2016)PermalinkPermalinkDiscrimination of deciduous tree species from time series of unmanned aerial system imagery / Jonathan Lisein in Plos one, vol 10 n° 11 (November 2015)PermalinkWide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing / Jerome O’Connell in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkAutomatic identification of building types based on topographic databases – a comparison of different data sources / Robert Hecht in International journal of cartography, vol 1 n° 1 (August 2015)Permalink