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Télédétection et photogrammétrie pour l'étude de la dynamique de l’occupation du sol dans le bassin versant de l’oued Chiba (Cap-Bon, Tunisie) / Anis Gasmi in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)
[article]
Titre : Télédétection et photogrammétrie pour l'étude de la dynamique de l’occupation du sol dans le bassin versant de l’oued Chiba (Cap-Bon, Tunisie) Type de document : Article/Communication Auteurs : Anis Gasmi, Auteur ; Antoine Masse , Auteur ; Danielle Ducrot, Auteur ; Hédi Zouari, Auteur Année de publication : 2017 Article en page(s) : pp 43 - 51 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] barrage
[Termes IGN] bassin hydrographique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dynamique spatiale
[Termes IGN] image aérienne
[Termes IGN] image Landsat-TM
[Termes IGN] image Terra-ASTER
[Termes IGN] occupation du sol
[Termes IGN] oued
[Termes IGN] TunisieRésumé : (auteur) L'article présente les résultats de l'étude de l'évolution spatiale et temporelle de l'occupation du sol dans le bassin de l’Oued Chiba (200 km2) situé au Nord-Est de la Tunisie. Cette analyse est fondée sur une classification de données de télédétection (images Landsat TM des années 1992, 2011 et une image ASTER de l’année 2001) et de photographies aériennes à l’échelle de 1/20 000 (des années 1962, 1974 et 1984). La méthode de classification des images satellites utilisées est un classifieur supervisé de type séparateur à vastes marges (SVM) avec un noyau gaussien. L'analyse de l’occupation des sols sur une période de 49 ans se traduit par une augmentation de l’espace agricole (classe des cultures / jachères et la classe d'arboricultures) de 24 km2 en 1962 à 75 km2 en 2011. Ces changements d'occupation et d'utilisation du sol sont liés directement à la création du barrage de l’Oued Chiba en 1963. Numéro de notice : A2017-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.52638/rfpt.2017.157 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.52638/rfpt.2017.157 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86547
in Revue Française de Photogrammétrie et de Télédétection > n° 215 (mai - août 2017) . - pp 43 - 51[article]Hyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Hyperspectral band selection from statistical wavelet models Type de document : Article/Communication Auteurs : Siwei Feng, Auteur ; Yuki Itoh, Auteur ; Mario Parente, Auteur ; Marco F. Duarte, Auteur Année de publication : 2017 Article en page(s) : pp 2111 - 2123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chaîne de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] redondance de données
[Termes IGN] signature spectraleRésumé : (Auteur) High spectral resolution brings hyperspectral images with large amounts of information, which makes these images more useful in many applications than images obtained from traditional multispectral scanners with low spectral resolution. However, the high data dimensionality of hyperspectral images increases the burden on data computation, storage, and transmission; fortunately, the high redundancy in the spectral domain allows for significant dimensionality reduction. Band selection provides a simple dimensionality reduction scheme by discarding bands that are highly redundant, thereby preserving the structure of the data set. This paper proposes a new criterion for pointwise-ranking-based band selection that uses a nonhomogeneous hidden Markov chain (NHMC) model for redundant wavelet coefficients of each hyperspectral signature. The model provides a binary multiscale label that encodes semantic features that are useful to discriminate spectral types. A band ranking score considers the average correlation among the average NHMC labels for each band. We also test richer discrete-valued label vectors that provide a more finely grained quantization of spectral fluctuations. In addition, since band selection methods based on band ranking often ignore correlations in selected bands, we study the effect of redundancy elimination, applied on the selected features, on the performance of an example classification problem. Our experimental results also include an optional redundancy elimination step and test their effect on classification performance that is based on the selected bands. The experimental results also include a comparison with several relevant supervised band selection techniques. Numéro de notice : A2017-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2636850 En ligne : https://doi.org/10.1109/TGRS.2016.2636850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84717
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2111 - 2123[article]Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery / Clément Dechesne in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
[article]
Titre : Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; Valérie Gouet-Brunet , Auteur Année de publication : 2017 Projets : HYEP / Weber, Christiane Article en page(s) : pp 129 – 145 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification automatique
[Termes IGN] classification dirigée
[Termes IGN] délimitation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espèce végétale
[Termes IGN] extraction d'arbres
[Termes IGN] fusion d'images
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] peuplement forestier
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Forest stands are the basic units for forest inventory and mapping. Stands are defined as large forested areas (e.g., ⩾⩾2 ha) of homogeneous tree species composition and age. Their accurate delineation is usually performed by human operators through visual analysis of very high resolution (VHR) infra-red images. This task is tedious, highly time consuming, and should be automated for scalability and efficient updating purposes. In this paper, a method based on the fusion of airborne lidar data and VHR multispectral images is proposed for the automatic delineation of forest stands containing one dominant species (purity superior to 75%). This is the key preliminary task for forest land-cover database update. The multispectral images give information about the tree species whereas 3D lidar point clouds provide geometric information on the trees and allow their individual extraction. Multi-modal features are computed, both at pixel and object levels: the objects are individual trees extracted from lidar data. A supervised classification is then performed at the object level in order to coarsely discriminate the existing tree species in each area of interest. The classification results are further processed to obtain homogeneous areas with smooth borders by employing an energy minimum framework, where additional constraints are joined to form the energy function. The experimental results show that the proposed method provides very satisfactory results both in terms of stand labeling and delineation (overall accuracy ranges between 84% and 99%). Numéro de notice : A2017-116 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.011 Date de publication en ligne : 27/02/2017 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84511
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 129 – 145[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Transferability of multi- and hyperspectral optical biocrust indices / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
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Titre : Transferability of multi- and hyperspectral optical biocrust indices Type de document : Article/Communication Auteurs : Emilio Rodríguez-Caballero, Auteur ; P. Escribano, Auteur ; C. Olehowski, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 94 – 107 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] classification dirigée
[Termes IGN] état de surface du sol
[Termes IGN] image hyperspectrale
[Termes IGN] indice de détection
[Termes IGN] surface du solRésumé : (auteur) Biological soil crusts (biocrusts) are communities of cyanobacteria, algae, microfungi, lichens and bryophytes in varying proportions, which live within or immediately on top of the uppermost millimeters of the soil in arid and semiarid regions. As biocrusts are highly relevant for ecosystem processes like carbon, nitrogen, and water cycling, a correct characterization of their spatial distribution is required. Following this objective, considerable efforts have been devoted to the identification and mapping of biocrusts using remote sensing data, and several mapping indices have been developed. However, their transferability to different regions has only rarely been tested. In this study we investigated the transferability of two multispectral indices, i.e. the Crust Index (CI) and the Biological Soil Crust Index (BSCI), and two hyperspectral indices, i.e. the Continuum Removal Crust Identification Algorithm (CRCIA) and the Crust Development Index (CDI), in three sites dominated by biocrusts, but with differences in soil and vegetation composition. Whereas multispectral indices have been important and valuable tools for first approaches to map and classify biological soil crusts, hyperspectral data and indices developed for these allowed to classify biocrusts at much higher accuracy. While multispectral indices showed Kappa (κ) values below 0.6, hyperspectral indices obtained good classification accuracy (κ ∼ 0.8) in both the study area where they had been developed and in the newly tested region. These results highlight the capability of hyperspectral sensors to identify specific absorption features related to photosynthetic pigments as chlorophyll and carotenoids, but also the limitation of multispectral information to discriminate between areas dominated by biocrusts, vegetation or bare soil. Based on these results we conclude that remote sensing offers an important and valid tool to map biocrusts. However, the spectral similarity between the main surface components of drylands and biocrusts demand for mapping indices based on hyperspectral information to correctly map areas dominated by biocrusts at ecosystem scale. Numéro de notice : A2017-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84510
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 94 – 107[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Attribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
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Titre : Attribute profiles on derived features for urban land cover classification Type de document : Article/Communication Auteurs : Bharath Bhushan Damodaran, Auteur ; Joachim Höhle, Auteur ; Sébastien Lefèvre, Auteur Année de publication : 2017 Article en page(s) : pp 183 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attribut
[Termes IGN] classification dirigée
[Termes IGN] données auxiliaires
[Termes IGN] hauteur ellipsoïdale
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] indice de végétation
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] zone urbaineRésumé : (Auteur) This research deals with the automatic generation of 2D land cover maps of urban areas using very high resolution multispectral aerial imagery. The appropriate selection of classifier and attributes is important to achieve high thematic accuracies. In this paper, new attributes are generated to increase the discriminative power of auxiliary information provided by remote sensing images. The generated attributes are derived from the vegetation index and elevation information using morphological attribute profiles. The extended experimental evaluation and comparison of attribute profile-based mapping solutions is conducted to derive the optimal combinations of attributes required for classification and to understand the genericity of attributes on a range of classifiers, i.e., various combinations of attributes and classifiers. Experimental results with two high resolution images show that the proposed attributes derived on auxiliary information outperform the existing attribute profiles computed on original image and its principal components. Numéro de notice : A2017-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.183 En ligne : https://doi.org/10.14358/PERS.83.3.183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84422
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 183 - 193[article]Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkPermalinkCartographie 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)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkNew iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)PermalinkPré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR / Stéphane Guinard (2017)PermalinkSegmentation sémantique de données de télédétection multimodale : application aux peuplements forestiers / Clément Dechesne (2017)PermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (2017)PermalinkUtilisation d’image THR et drone pour l’étude de la dynamique côtière d’Ouvéa (Île des Loyautés - Nouvelle Calédonie) / Sabrina Bosque (2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)PermalinkDeveloping a web-based system for supervised classification of remote sensing images / Ziheng Sun in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkApport des images THRS pour la catégorisation des agro-systèmes complexes à Mayotte / Rafaël Molina in Géomatique expert, n° 111 (juillet- août 2016)PermalinkClassifying buildings from point clouds and images / Evangelos Maltezos in GIM international [en ligne], vol 30 n° 7 (July 2016)PermalinkObject-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery / Mary Pyott Freeman in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)PermalinkOptimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa / Romano Lottering in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkPrediction of categorical spatial data via Bayesian updating / Xiang Huang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkAn evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States / Jochen Wendel in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkContext-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)PermalinkA spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkActive-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkStreet-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)PermalinkClassified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkData fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery / Biswajeet Pradhan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkMulti-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkA joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)PermalinkForest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)PermalinkPermalinkPermalinkVegetation classification and biogeography of European floodplain forests and alder carrs / Jan Douda in Applied Vegetation Science, vol 19 n° 1 (January 2016)PermalinkAutomated annual cropland mapping using knowledge-based temporal features / François Waldner in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkSemi-supervised SVM for individual tree crown species classification / Michele Dalponte in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkUrban classification by the fusion of thermal infrared hyperspectral and visible data / Jiayi Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)PermalinkInvestigating 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)PermalinkMeasuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery / X. Chen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkDétection à haute résolution spatiale de la desserte forestière en milieu montagneux / António Ferraz in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)PermalinkEstimation de la déforestation des forêts humides à Madagascar utilisant une classification multidate d'images Landsat entre 2005, 2010 et 2013 / F.A. Rakotomala in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)PermalinkOperationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)PermalinkIntegrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkComplementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkData-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)PermalinkDélimitation des parcelles agricoles par classification d'images Pléiades / Nesrine Chehata in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)Permalink