Centre de documentation
scientifique

Land cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 348 - 374 Titre : Land cover harmonization using Latent Dirichlet Allocation Type de document : Article/Communication Auteurs : Zhan Li, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2021 Article en page(s) : pp 348 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] Canada [Termes IGN] carte d'occupation du sol [Termes IGN] chevauchement [Termes IGN] erreur de classification [Termes IGN] harmonisation des données [Termes IGN] matrice d'erreur [Termes IGN] matrice de co-occurrence [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol Résumé : (auteur) Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas. Numéro de notice : A2021-027 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1796131 Date de publication en ligne : 27/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1796131 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96701 [article]

Multiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6281 - 6297 Titre : Multiscale supervised kernel dictionary learning for SAR target recognition Type de document : Article/Communication Auteurs : Lei Tao, Auteur ; Xue Jiang, Auteur ; Xingzhao Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6281 - 6297 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] détection de cible [Termes IGN] erreur de classification [Termes IGN] image radar moirée [Termes IGN] méthode fondée sur le noyau [Termes IGN] reconstruction d'image Résumé : (auteur) In this article, a supervised nonlinear dictionary learning (DL) method, called multiscale supervised kernel DL (MSK-DL), is proposed for target recognition in synthetic aperture radar (SAR) images. We use Frost filters with different parameters to extract an SAR image’s multiscale features for data augmentation and noise suppression. In order to reduce the computation cost, the dimension of each scale feature is reduced by principal component analysis (PCA). Instead of the widely used linear DL, we learn multiple nonlinear dictionaries to capture the nonlinear structure of data by introducing the dimension-reduced features into the nonlinear reconstruction error terms. A classification model, which is defined as a discriminative classification error term, is learned simultaneously. Hence, the objective function contains the nonlinear reconstruction error terms and a classification error term. Two optimization algorithms, called multiscale supervised kernel K-singular value decomposition (MSK-KSVD) and multiscale supervised incremental kernel DL (MSIK-DL), are proposed to compute the multidictionary and the classifier. Experiments on the moving and stationary target automatic recognition (MSTAR) data set are performed to evaluate the effectiveness of the two proposed algorithms. And the experimental results demonstrate that the proposed scheme outperforms some representative common machine learning strategies, state-of-the-art convolutional neural network (CNN) models and some representative DL methods, especially in terms of its robustness against training set size and noise. Numéro de notice : A2020-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976203 Date de publication en ligne : 03/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95709 [article]

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)  

Public [article]  inSilva fennica > vol 54 n° 2 (March 2020) Titre : An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data Type de document : Article/Communication Auteurs : Olga Grigorieva, Auteur ; Olga Brovkina, Auteur ; Alisher Saidov, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Betula (genre) [Termes IGN] carte forestière [Termes IGN] classification [Termes IGN] erreur de classification [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] phénologie [Termes IGN] Pinus (genre) [Termes IGN] réflectance spectrale [Termes IGN] République Tchèque [Termes IGN] Russie [Termes IGN] signature spectrale [Termes IGN] variation saisonnière Résumé : (auteur) his study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time. Numéro de notice : A2020-324 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10143 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.14214/sf.10143 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95198 [article]

Changement climatique et risque inondation / William Halbecq in Géomatique expert, n° 119 (novembre - décembre 2017)

Public [article]  inGéomatique expert > n° 119 (novembre - décembre 2017) . - pp 36 - 43 Titre : Changement climatique et risque inondation Type de document : Article/Communication Auteurs : William Halbecq, Auteur ; Nicolas Bauduceau, Auteur ; Camille Rossi, Organisateur de réunion Année de publication : 2017 Article en page(s) : pp 36 - 43 Note générale : Entretien organisé par Business Geographics Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] cartographie dynamique [Termes IGN] changement climatique [Termes IGN] crue [Termes IGN] données lidar [Termes IGN] enjeu [Termes IGN] erreur de classification [Termes IGN] historique des données [Termes IGN] image aérienne [Termes IGN] inondation [Termes IGN] modélisation spatiale [Termes IGN] photo-interprétation [Termes IGN] plan de prévention des risques [Termes IGN] précision des données [Termes IGN] risque naturel [Termes IGN] submersion marine [Termes IGN] système d'information géographique Résumé : (Auteur) William Halbecq et Nicolas Bauduceau, experts en risques, discutent de la réalité du changement climatique et ce que cela implique pour les risques de crues et de submersion marine. Numéro de notice : A2017-773 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88827 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
265-2017061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
IFN-001-P002007	PER	Revue	Nogent-sur-Vernisson	Salle périodiques	Exclu du prêt

Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 150 - 161 Titre : Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data Type de document : Article/Communication Auteurs : Timo P Pitkänen, Auteur ; Niina Käyhkö, Auteur Année de publication : 2017 Article en page(s) : pp 150 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse diachronique [Termes IGN] arbre (flore) [Termes IGN] boisement naturel [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité des points [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur de classification [Termes IGN] image Landsat [Termes IGN] orthoimage [Termes IGN] prairie [Termes IGN] structure de données localisées Résumé : (Auteur) Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to identify structurally oriented and dynamically organized landscape phenomena, such as grassland overgrowth. Numéro de notice : A2017-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86459 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Cartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)  

Permalink

Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery / Iman Khosravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)  

Permalink

Hyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)  

Permalink

An edge-oriented approach to thematic map error assessment / S. Sweeney in Geocarto international, vol 27 n° 1 (February 2012)  

Permalink

Sensivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model / Zhi Huang in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)  

Permalink

Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping / D. Hester in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 4 (April 2008)  

Permalink

Détection des haies et segmentation automatique / A. Dommanget in Géomatique expert, n° 57 (01/07/2007)

Permalink

Spatial PSF nonuniformity effects in airborne pushbroom imaging spectrometry data / Daniel Schläpfer in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)  

Permalink

Resolution dependent errors in remote sensing of cultivated areas / M. Ozdogan in Remote sensing of environment, vol 103 n° 2 (30/07/2006)  

Permalink

On merging high- and low-resolution DEMs from TOPSAR and SRTM using a prediction-error filter / S. Yun in IEEE Transactions on geoscience and remote sensing, vol 43 n° 7 (July 2005)  

Permalink