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Extension of the linear chromodynamics model for spectral change detection in the presence of residual spatial misregistration / Karmon Vongsy in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
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Titre : Extension of the linear chromodynamics model for spectral change detection in the presence of residual spatial misregistration Type de document : Article/Communication Auteurs : Karmon Vongsy, Auteur ; Michael T. Eismann, Auteur ; Michael J. Mendenhall, Auteur Année de publication : 2015 Article en page(s) : pp 3005 - 3021 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approximation
[Termes IGN] détection de changement
[Termes IGN] distribution de Gauss
[Termes IGN] image hyperspectrale
[Termes IGN] modèle linéaire
[Termes IGN] résiduRésumé : (Auteur) A generalized likelihood ratio test (GLRT) statistic for spectral change detection based on the linear chromodynamics model is extended to accommodate unknown residual misregistration between imagery described by a prior probability density function for the spatial misregistration. Using a normal prior distribution leads to a fourth-order polynomial that can be numerically minimized over the unknown misregistration parameters. A more computationally efficient closed-form solution is developed based on a quadratic approximation and provides comparable results to the numerical minimization for the investigated test cases while running 30 times faster. The results applying the method to hyperspectral imagery indicate up to an order of magnitude reduction in false alarms at the same detection rate relative to baseline change detection methods for synthetically misregistered test data particularly in image regions containing edges and fine spatial features. Sensitivity to model parameters is assessed, and the method is compared with a previously published misregistration compensation approach yielding comparable results. Although the GLRT approach appears to exhibit comparable change detection performance, it offers the possibility of tailoring the algorithm to a priori knowledge of expected misregistration errors or to compensate structured misregistration as would occur due to parallax errors due to perspective variations (e.g., image parallax). Numéro de notice : A2015-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2367471 Date de publication en ligne : 18/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2367471 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76398
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3005 - 3021[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Fast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)
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Titre : Fast forward feature selection of hyperspectral images for classification with gaussian mixture models Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Clément Dechesne , Auteur ; Anthony Zullo, Auteur ; Frédéric Ferraty, Auteur
Année de publication : 2015 Article en page(s) : pp 2824 - 2831 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Gauss
[Termes IGN] apprentissage automatique
[Termes IGN] classificateur
[Termes IGN] classification
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] itérationRésumé : (auteur) A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation (k-CV). In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, submodels can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels. Numéro de notice : A2015--068 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/JSTARS.2015.2441771 En ligne : http://dx.doi.org/10.1109/JSTARS.2015.2441771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83227
in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 8 n° 6 (June 2015) . - pp 2824 - 2831[article]A fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
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Titre : A fuzzy spatial reasoner for multi-scale GEOBIA ontologies Type de document : Article/Communication Auteurs : Argyros Argyridis, Auteur ; Demetre P. Argialas, Auteur Année de publication : 2015 Article en page(s) : pp 491 - 498 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification à base de connaissances
[Termes IGN] détection du bâti
[Termes IGN] image Quickbird
[Termes IGN] objet géographique
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] PostgreSQL
[Termes IGN] segmentation d'image
[Termes IGN] toitRésumé : (auteur) In Geographic Object-Based Image Analysis (GEOBIA), an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/ supervised methods, or knowledge-based methods, such as an ontology. The aim of this paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL 2) with fuzzy representations was adopted and expanded to represent fuzzy spatial relationships within the framework of GEOBIA. Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework, a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected. Numéro de notice : A2015-979 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.491 En ligne : https://doi.org/10.14358/PERS.81.6.491 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80062
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 491 - 498[article]A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data / Victor F. Strimbu in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)
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Titre : A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data Type de document : Article/Communication Auteurs : Victor F. Strimbu, Auteur ; Bogdan M. Strimbu, Auteur Année de publication : 2015 Article en page(s) : pp 30 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] graphe
[Termes IGN] houppier
[Termes IGN] Louisiane (Etats-Unis)
[Termes IGN] segmentation
[Termes IGN] structure hiérarchique de donnéesRésumé : (auteur) This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures.
The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m2.
More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.Numéro de notice : A2015-699 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.018 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78335
in ISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 30 - 43[article]Integrating 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)
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Titre : Integrating user needs on misclassification error sensitivity into image segmentation quality assessment Type de document : Article/Communication Auteurs : Hugo Costa, Auteur ; Giles M. Foody, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2015 Article en page(s) : pp 451 - 459 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des besoins
[Termes IGN] classification dirigée
[Termes IGN] connaissance thématique
[Termes IGN] objet géographique
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] similitude
[Termes IGN] utilisateurRésumé : (auteur) Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach Numéro de notice : A2015-976 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.451 En ligne : https://doi.org/10.14358/PERS.81.6.451 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80059
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 451 - 459[article]Invariant rules for multipolarization SAR change detection / Vincenzo Carotenuto in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkMangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
PermalinkObject-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
PermalinkObject detection in optical remote sensing images based on weakly supervised learning and high-level feature learning / Junwei Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkSemi-automated building footprint extraction from orthophotos / Rheannon Brooks in Geomatica, vol 69 n° 2 (June 2015)
PermalinkSubstance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkVery high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 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)
PermalinkForest 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)
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