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Slow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
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[article]
Titre : Slow feature analysis for change detection in multispectral imagery Type de document : Article/Communication Auteurs : Chen Wu, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 2858 - 2874 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de variance
[Termes IGN] détection de changement
[Termes IGN] image Landsat-ETM+
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] pixel
[Termes IGN] varianceRésumé : (Auteur) Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods. Numéro de notice : A2014-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2266673 En ligne : https://doi.org/10.1109/TGRS.2013.2266673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33167
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2858 - 2874[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014051A RAB Revue Centre de documentation En réserve L003 En circulation
Exclu du prêtWetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar / Lian P. Rampi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)
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[article]
Titre : Wetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar Type de document : Article/Communication Auteurs : Lian P. Rampi, Auteur ; Joseph F. Knight, Auteur ; Keith C. Pelletier, Auteur Année de publication : 2014 Article en page(s) : pp 439 - 449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] image radar
[Termes IGN] marais
[Termes IGN] Minnesota (Etats-Unis)Résumé : (Auteur) This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer's and user's accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods. Numéro de notice : A2014-243 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.5.439 En ligne : https://doi.org/10.14358/PERS.80.5.439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33146
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 5 (May 2014) . - pp 439 - 449[article]Bayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
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Titre : Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Christopher Ratto, Auteur ; Kenneth D. Morton, Auteur ; Leslie M. Collins, Auteur ; Peter A. Torrione, Auteur Année de publication : 2014 Article en page(s) : pp 1969 - 1981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification contextuelle
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] rayonnement infrarougeRésumé : (Auteur) Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. Numéro de notice : A2014-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257175 En ligne : https://doi.org/10.1109/TGRS.2013.2257175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33170
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1969 - 1981[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Change detection in high-resolution land use/land cover geodatabases (at object level) / Emilio Domenech (01/04/2014)
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contenu dans Change detection in high-resolution land use/land cover geodatabase (at object level) / European Spatial Data Research EuroSDR (2014)
Titre : Change detection in high-resolution land use/land cover geodatabases (at object level) Type de document : Chapitre/Contribution Auteurs : Emilio Domenech, Auteur ; Clément Mallet , Auteur
Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 01/04/2014 Collection : EuroSDR official publication, ISSN 0257-0505 num. 64 Importance : pp 10 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] base de données thématiques
[Termes IGN] base de données topographiques
[Termes IGN] changement d'occupation du sol
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification automatique d'objets
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] données multisources
[Termes IGN] niveau de détail
[Termes IGN] occupation du sol
[Termes IGN] utilisation du solNuméro de notice : H2014-001 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65905 Documents numériques
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eurosdr_no64_a-1_1.pdfAdobe Acrobat PDFProgressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
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Titre : Progressive band selection of spectral unmixing for hyperspectral imagery Type de document : Article/Communication Auteurs : Chein-I Chang, Auteur ; Keng-Hao Liu, Auteur Année de publication : 2014 Article en page(s) : pp 2002 - 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectraleRésumé : (Auteur) A new band selection (BS), called progressive BS (PBS) of spectral unmixing for hyperspectral imagery is being presented. It is quite different from the traditional BS in the sense that the former adapts the number of selected bands, p to various endmembers used for spectral unmixing, while the latter fixes the value of p at a constant for all endmembers. Due to the fact that different endmembers post various levels of difficulty in discrimination, each endmember should have its own custom-selected bands to specify its spectral characteristics. In order to address this issue, p is composed of two values, one value determined by virtual dimensionality to accommodate each of endmembers and the other is determined by a new concept of band dimensionality allocation to account for discrminability among endmembers. In order to find appropriate bands to be used for PBS, band prioritization and band de-correlation are included to rank bands according to significance of band information and to remove interband redundancy, respectively. As a result, spectral unmixing can be performed progressively by selecting different bands for various endmembers, a task that the traditional BS cannot accomplish. The effectiveness and advantages of using PBS over BS are also demonstrated by experiments. Numéro de notice : A2014-268 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257604 En ligne : https://doi.org/10.1109/TGRS.2013.2257604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33171
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 2002 - 2017[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Reconstruct street network from imprecise excavation data using fuzzy Hough transforms / Cyril de Runz in Geoinformatica, vol 18 n° 2 (April 2014)
PermalinkSegmentation d'images aériennes par coopération LPE-régions et LPE-contours, application à la caractérisation de toitures / Youssef El Merabet in Revue Française de Photogrammétrie et de Télédétection, n° 206 (Avril 2014)
PermalinkJoining-up the dots / George Skrobanski in GEO: Geoconnexion international, vol 13 n° 3 (march 2014)
PermalinkSemi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)
PermalinkA study-based ranking of LiDAR data visualization schemes aided by georectified aerial images / Suddasheel Ghosh in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)
PermalinkSupervised change detection in satellite imagery using super pixels and relevance feedback / Surender Varma Gadhiraju in Geomatica, vol 68 n° 1 (March 2014)
PermalinkAdaptive subpixel mapping based on a multiagent system for remote-sensing imagery / Xiong Xu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkApplication of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India / L.P. Sharma in Geocarto international, vol 29 n° 1 - 2 (February - April 2014)
PermalinkAutomated parameterisation for multi-scale image segmentation on multiple layers / L. Drăguț in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkBi-temporal texton forest for land cover transition detection on remotely sensed imagery / Zhen Lei in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkBlind speckle decorrelation for SAR image despeckling / Alessandro Lapini in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkDetecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm / Abduwasit Ghulam in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkA fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkModel-based analysis–synthesis for realistic tree reconstruction and growth simulation / Corina Iovan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkMulti-agent recognition system based on object based image analysis using WorldView-2 / Fatemeh Tabib Mahmoudi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)
PermalinkMultiagent object-based classifier for high spatial resolution imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkMultiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkNonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization / Naoto Yokoya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkStructured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
PermalinkPermalinkAssessment of the image misregistration effects on object-based change detection / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)
PermalinkChange detection in high-resolution land use/land cover geodatabase (at object level) / European Spatial Data Research EuroSDR (2014)
PermalinkCollaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)
PermalinkComparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)
PermalinkContextual classification of lidar data and building object detection in urban areas / Joachim Niemeyer in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkDétection de bâtiments à partir d’une image satellitaire par combinaison d’approches ascendante et descendante / Mohamed Mahmoud Sidi Yousseff (2014)
PermalinkPermalinkPermalinkFast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)
PermalinkGénération de modèles numériques de surface et détection de changements 3D à partir d'imagerie satellite stéréoscopique très haute résolution / Cyrielle Guerin (2014)
PermalinkGeographic Object-Based Image Analysis: Towards a new paradigm / Thomas Blaschke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkHierarchical extraction of landslides from multiresolution remotely sensed optical images / Camille Kurtz in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkPermalinkIndividual tree segmentation over large areas using airborne LiDAR point cloud and very high resolution optical imagery / Yuchu Qin (2014)
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PermalinkPermalinkLarge scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)
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PermalinkLiDAR-derived surface roughness texture mapping: Application to mount St. Helens Pumice Plain deposit analysis / Patrick L. Whelley in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)
PermalinkA local contrast method for small infrared target detection / C.L. Philip Chen in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)
PermalinkMise à jour d’une base de données d’occupation du sol à grande échelle en milieux naturels à partir d’une image satellite THR / Adrien Gressin (2014)
PermalinkPermalinkReconstruction de modèles 3D photoréalistes de façades à partir de données image et laser terrestre / Jérôme Demantké (2014)
PermalinkRemote sensing image segmentation by combining spectral and texture features / H. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
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