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Hyperspectral feature extraction using total variation component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6976 - 6985 Titre : Hyperspectral feature extraction using total variation component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6976 - 6985 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] extraction automatique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods. Numéro de notice : A2016-922 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2593463 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2593463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83326 [article]

Automatic extraction of road networks from GPS traces / Jia Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 8 (August 2016) . - pp 593 - 604 Titre : Automatic extraction of road networks from GPS traces Type de document : Article/Communication Auteurs : Jia Qiu, Auteur ; Ruisheng Wang, Auteur Année de publication : 2016 Article en page(s) : pp 593 - 604 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse de groupement [Termes IGN] appariement de points [Termes IGN] compensation par moindres carrés [Termes IGN] données GPS [Termes IGN] extraction automatique [Termes IGN] extraction de données [Termes IGN] modèle de Markov [Termes IGN] relation topologique [Termes IGN] réseau routier [Termes IGN] segmentation Résumé : (auteur) We propose a point segmentation and grouping method to generate road maps from GPS traces. First, we present a progressive point cloud segmentation algorithm based on Total Least Squares (TLS) line fitting. Second, we group topologically connected point clusters by the point's orientation and cluster's spatial proximity, where the topological relationship is generated using Hidden Markov Model (HMM) map matching. Finally, we refine the intersections of roads so that their geometrical and topological relationships are consistent with each other. Experimental results show that our algorithm is robust to noises and the generated road network has a high accuracy in terms of geometry and topology. Compare to the representative algorithms; the results of our new algorithm have a higher F-measure score for different matching thresholds. Numéro de notice : A2016-606 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.14358/PERS.82.8.593 En ligne : http://dx.doi.org/10.14358/PERS.82.8.593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81796 [article]

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Supervised 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3722 - 3735 Titre : Supervised classification of very high resolution optical images using wavelet-based textural features Type de document : Article/Communication Auteurs : Olivier Regniers, Auteur ; Lionel Bombrun, Auteur ; Virginie Lafon, Auteur ; Christian Germain, Auteur Année de publication : 2016 Article en page(s) : pp 3722 - 3735 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] classification dirigée [Termes IGN] extraction automatique [Termes IGN] image à très haute résolution [Termes IGN] image optique [Termes IGN] image panchromatique [Termes IGN] image Pléiades [Termes IGN] texture d'image Résumé : (Auteur) In this paper, we explore the potentialities of using wavelet-based multivariate models for the classification of very high resolution optical images. A strategy is proposed to apply these models in a supervised classification framework. This strategy includes a content-based image retrieval analysis applied on a texture database prior to the classification in order to identify which multivariate model performs the best in the context of application. Once identified, the best models are further applied in a supervised classification procedure by extracting texture features from a learning database and from regions obtained by a presegmentation of the image to classify. The classification is then operated according to the decision rules of the chosen classifier. The use of the proposed strategy is illustrated in two real case applications using Pléiades panchromatic images: the detection of vineyards and the detection of cultivated oyster fields. In both cases, at least one of the tested multivariate models displays higher classification accuracies than gray-level cooccurrence matrix descriptors. Its high adaptability and the low number of parameters to be set are other advantages of the proposed approach. Numéro de notice : A2016-858 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2526078 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2526078 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83002 [article]

Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2615 - 2626 Titre : Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning Type de document : Article/Communication Auteurs : Claudio Persello, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2016 Article en page(s) : pp 2615 - 2626 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] extraction automatique [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau [Termes IGN] séparateur à vaste marge Résumé : (Auteur) This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities. Numéro de notice : A2016-843 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2503885 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2503885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82887 [article]

Active-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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 1925 - 1939 Titre : Active-metric learning for classification of remotely sensed hyperspectral images Type de document : Article/Communication Auteurs : Edoardo Pasolli, Auteur ; Hsiuhan Lexie Yang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2016 Article en page(s) : pp 1925 - 1939 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques Résumé : (Auteur) Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with k-nearest neighbor ( k-NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL. Numéro de notice : A2016-836 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2490482 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2490482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82880 [article]

Fusion of hyperspectral images and digital surface models for urban object extraction / Janja Avbelj (2016) 

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Mise en place de procédures automatiques en vue d’accélérer la production des plans topographiques au sein de l’entreprise Techni Drone / Kévin Javerliat (2016)

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Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)  

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Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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Measuring 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)  

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Semisupervised transfer component analysis for domain adaptation in remote sensing image classification / Giona Matasci in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

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Vectorisation automatique des forêts dans les minutes de la carte d’état-major du 19e siècle / Pierre-Alexis Herrault in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)  

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vol 25 n° 1 - mars - mai 2015 - Traitement de l'information et prospective (Bulletin de Revue internationale de géomatique) / Françoise Gourmelon  

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In-flight photogrammetric camera calibration and validation via complementary lidar / A.S. Gneeniss in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / Daniela Poli in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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