Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > analyse texturale
analyse texturaleVoir aussi |
Documents disponibles dans cette catégorie (299)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
[article]
Titre : Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas Type de document : Article/Communication Auteurs : Timothy Dube, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2015 Article en page(s) : pp 12 – 32 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] Afrique du sud (état)
[Termes IGN] biodiversité
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] classification
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] Eucalyptus dunii
[Termes IGN] Eucalyptus grandis
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] Pinus taeda
[Termes IGN] régression
[Termes IGN] sous-étage
[Termes IGN] sylviculture
[Termes IGN] texture d'imageRésumé : (auteur) The successful launch of the 30-m Landsat-8 Operational Land Imager (OLI) pushbroom sensor offers a new primary data source necessary for aboveground biomass (AGB) estimation, especially in resource-limited environments. In this work, the strength and performance of Landsat-8 OLI image derived texture metrics (i.e. texture measures and texture ratios) in estimating plantation forest species AGB was investigated. It was hypothesized that the sensor’s pushbroom design, coupled with the presence of refined spectral properties, enhanced radiometric resolution (i.e. from 8 bits to 12 bits) and improved signal-to-noise ratio have the potential to provide detailed spectral information necessary for significantly strengthening AGB estimation in medium-density forest canopies. The relationship between image texture metrics and measurements of forest attributes can be used to help characterize complex forests, and enhance fine vegetation biophysical properties, a difficult challenge when using spectral vegetation indices especially in closed canopies. This study examines the prospects of using Landsat-8 OLI sensor derived texture metrics for estimating AGB for three medium-density plantation forest species in KwaZulu Natal, South Africa. In order to achieve this objective, three unique data pre-processing techniques were tested (analysis I: Landsat-8 OLI raw spectral-bands vs. raw texture bands; analysis II: Landsat-8 OLI raw spectral-band ratios vs. texture band ratios and analysis III: Landsat-8 OLI derived vegetation indices vs. texture band ratios). The landsat-8 OLI derived texture parameters were examined for robustness in estimating AGB using linear regression, stepwise-multiple linear regression and stochastic gradient boosting regression models. The results of this study demonstrated that all texture parameters particularly band texture ratios calculated using a 3 × 3 window size, could enhance AGB estimation when compared to simple spectral reflectance, simple band ratios and the most popular spectral vegetation indices. For instance, the use of combined texture ratios yielded the highest R2 values of 0.76 (RMSE = 9.55 t ha−1 (18.07%) and CV-RMSE of 0.18); 0.74 (RMSE = 12.81 t ha−1 (17.72%) and CV-RMSE of 0.08); 0.74 (RMSE = 12.67 t ha−1 (06.15%) and CV-RMSE of 0.06) and 0.53 (RMSE = 20.15 t ha−1 (14.40%) and CV-RMSE of 0.15) overall for Eucalyptus dunii, Eucalyptus grandis, Pinus taeda individually and all species, respectively. Overall, the findings of this study provide the necessary insight and motivation to the remote sensing community, particularly in resource constrained regions, to shift towards embracing various texture metrics obtained from the readily-available and cheap multispectral Landsat-8 OLI sensor. Numéro de notice : A2015-849 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.06.002 Date de publication en ligne : 25/06/2015 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.06.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79219
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 12 – 32[article]Color and texture interpolation between orthoimagery and vector data / Charlotte Hoarau in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)
[article]
Titre : Color and texture interpolation between orthoimagery and vector data Type de document : Article/Communication Auteurs : Charlotte Hoarau , Auteur ; Sidonie Christophe , Auteur Année de publication : 2015 Conférence : ISPRS 2015, Geospatial Week : Laserscanning, ISSDQ, CMRT, ISA, GeoVIS, GeoBigData 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Annals, GeoVIS 2015 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Annals Article en page(s) : pp 507 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] couleur à l'écran
[Termes IGN] données vectorielles
[Termes IGN] interpolation
[Termes IGN] orthoimage couleur
[Termes IGN] rendu réaliste
[Termes IGN] représentation continue
[Termes IGN] représentation des données
[Termes IGN] superposition
[Termes IGN] texture d'image
[Termes IGN] visualisation de données
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Graphic interfaces of geoportals allow visualizing, sometimes overlaying often by image blending, various data and representations of geographical spaces: vector data, maps, aerial imagery, Digital Terrain Model (DTM), etc. After some previous works it appears that image blending is insufficient to allow efficient co-visualization of orthoimagery and vector data. Our purpose is to be able to manage an hybrid visualization of orthoimagery and vector data, efficient and useful. The diversity of hybridization levels requires to be able to control a continuum between such data. We thus have to propose rendering methods to mix heterogeneous data, in order to propose homogeneous and continuous intermediary representations. This paper proposes a methodology to interpolate graphic parameters between an orthoimage and related vector data, to control the level of orthophotorealism, all along the continuum. We detail the methodology based on several interpolation components, to control abstraction and realism levels, by manipulating colors and textures (natural, procedural or mixed). Numéro de notice : A2015--050 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W5-507-2015 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W5-507-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82611
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W5 (October 2015) . - pp 507 - 514[article]Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
[article]
Titre : Local binary patterns and extreme learning machine for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Chen Chen, Auteur ; Hongjun Su, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 3681 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification spectrale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] texture d'imageRésumé : (Auteur) It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. Numéro de notice : A2015-316 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381602 En ligne : https://doi.org/10.1109/TGRS.2014.2381602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76566
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3681 - 3693[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
[article]
Titre : Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features Type de document : Article/Communication Auteurs : Peijun Du, Auteur ; Alim Samat, Auteur ; Björn Waske, Auteur ; Sicong Liu, Auteur ; Zhenhong Li, Auteur Année de publication : 2015 Article en page(s) : pp 38 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] image Radarsat
[Termes IGN] polarimétrie radar
[Termes IGN] Rotation Forest classification
[Termes IGN] texture d'imageRésumé : (auteur) Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. Numéro de notice : A2015-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78342
in ISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 38 - 53[article]Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)
[article]
Titre : Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation Type de document : Article/Communication Auteurs : J.G. Martins, Auteur ; L.S. Oliveira, Auteur ; A.S. Britto Jr, Auteur ; Robert Sabourin, Auteur Année de publication : 2015 Article en page(s) : pp 279 - 293 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] arbre (flore)
[Termes IGN] base de données d'images
[Termes IGN] classificateur
[Termes IGN] données vectorielles
[Termes IGN] reconnaissance d'objets
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texture-based features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior. Numéro de notice : A2015--098 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s00138-015-0659-0 Date de publication en ligne : 29/01/2015 En ligne : http://doi.org/10.1007/s00138-015-0659-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85410
in Machine Vision and Applications > vol 26 n° 2-3 (April 2015) . - pp 279 - 293[article]Contribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)PermalinkDémélange d’images radar polarimétrique par séparation thématique de sources / Sébastien Giordano (2015)PermalinkPrédire la structure des forêts tropicales humides calédoniennes : analyse texturale de la canopée sur des images Pléiades / Elodie Blanchard in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)PermalinkPermalinkSAR-SIFT : a SIFT-like algorithm for SAR images / Flora Dellinger in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkVisual accceptance of library-generated CityGML LOD3 building models / Ryan Garnett in Cartographica, vol 49 n° 4 (Winter 2014)PermalinkQuantification et cartographie de la structure forestière à partir de la texture des images Pléiades / Benoit Beguet in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)PermalinkThe potential of Pléiades imagery for vegetation mapping: a case study of plain and mountainous open environments / Vincent Thierion in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)PermalinkSAR change detection based on intensity and texture changes / Maoguo Gong in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)PermalinkBlind speckle decorrelation for SAR image despeckling / Alessandro Lapini in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)Permalink