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Classification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 833 - 843 Titre : Classification and reconstruction from random projections for hyperspectral imagery Type de document : Article/Communication Auteurs : W. Li, Auteur ; S. Prasad, Auteur ; J. Fowler, Auteur Année de publication : 2013 Article en page(s) : pp 833 - 843 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse en composantes principales [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] image hyperspectrale [Termes IGN] reconstruction d'image Résumé : (Auteur) There is increasing interest in dimensionality reduction through random projections due in part to the emerging paradigm of compressed sensing. It is anticipated that signal acquisition with random projections will decrease signal-sensing costs significantly; moreover, it has been demonstrated that both supervised and unsupervised statistical learning algorithms work reliably within randomly projected subspaces. Capitalizing on this latter development, several class-dependent strategies are proposed for the reconstruction of hyperspectral imagery from random projections. In this approach, each hyperspectral pixel is first classified into one of several pixel groups using either a conventional supervised classifier or an unsupervised clustering algorithm. After the grouping procedure, a suitable reconstruction method, such as compressive projection principal component analysis, is employed independently within each group. Experimental results confirm that such class-dependent reconstruction, which employs statistics pertinent to each class as opposed to the global statistics estimated over the entire data set, results in more accurate reconstructions of hyperspectral pixels from random projections. Numéro de notice : A2013-082 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2204759 En ligne : https://doi.org/10.1109/TGRS.2012.2204759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32220 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3474 - 3486 Titre : Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification Type de document : Article/Communication Auteurs : S. Prasad, Auteur ; J. Fowler, Auteur ; L. Bruce, Auteur ; W. Li, Auteur Année de publication : 2012 Article en page(s) : pp 3474 - 3486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] filtrage du bruit [Termes IGN] fusion de données [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] partitionnement [Termes IGN] redondance de données [Termes IGN] transformation en ondelettes Résumé : (Auteur) Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches. Numéro de notice : A2012-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185053 Date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31897 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

A framework for supervised image classification with incomplete training samples / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 6 (June 2012) . - pp 595 - 604 Titre : A framework for supervised image classification with incomplete training samples Type de document : Article/Communication Auteurs : Q. Guo, Auteur ; W. Li, Auteur ; J. Chen, Auteur Année de publication : 2012 Article en page(s) : pp 595 - 604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] échantillonnage d'image [Termes IGN] extraction de couche Résumé : (Auteur) For traditional supervised classification methods, all land-cover types need to be exhaustively labeled to train the classifier. However, there are situations where the training sample classes are incomplete due to a lack of understanding of ground cover types in the image. In this study we propose a one-by-one (OBO) classification framework to address this incomplete training sample problem. The OBO approach is based on a one-class classifier (positive and unlabeled learning algorithm), and it extracts the land-cover type from the image one at a time. The performance of the proposed method was compared with a traditional supervised classifier using a high spatial resolution image. The average accuracy of the new method is 76.34 percent across different training sample sizes, whereas the accuracy of the classical approach is 66.46 percent, with an increase of 9.88 percent. The results demonstrate that the proposed new framework provides significantly higher classification accuracy than the classical approach at the 95 percent confidence level, and shows promise in dealing with the incomplete training sample problem for supervised image classification. Numéro de notice : A2012-249 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.6.595 En ligne : https://doi.org/10.14358/PERS.78.6.595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31695 [article]

An active crawler for discovering geospatial Web services and their distribution pattern : A case study of OGC Web Map Service / W. Li in International journal of geographical information science IJGIS, vol 24 n°7-8 (july 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n°7-8 (july 2010) . - pp 1127 - 1147 Titre : An active crawler for discovering geospatial Web services and their distribution pattern : A case study of OGC Web Map Service Type de document : Article/Communication Auteurs : W. Li, Auteur ; C. Yang, Auteur Année de publication : 2010 Article en page(s) : pp 1127 - 1147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] service web géographique [Termes IGN] Web Map Service Résumé : (Auteur) The increased popularity of standards for geospatial interoperability has led to an increasing number of geospatial Web services (GWSs), such as Web Map Services (WMSs), becoming publicly available on the Internet. However, finding the services in a quick and precise fashion is still a challenge. Traditional methods collect the services through centralized registries, where services can be manually registered. But the metadata of the registered services cannot be updated timely. This paper addresses the above challenges by developing an effective crawler to discover and update the services in (1) proposing an accumulated term frequency (ATF)-based conditional probability model for prioritized crawling, (2) utilizing concurrent multi-threading technique, and (3) adopting an automatic mechanism to update the metadata of identified services. Experiments show that the proposed crawler achieves good performance in both crawling efficiency and results' coverage/liveliness. In addition, an interesting finding regarding the distribution pattern of WMSs is discussed. We expect this research to contribute to automatic GWS discovery over the large-scale and dynamic World Wide Web and the promotion of operational interoperable distributed geospatial services. Numéro de notice : A2010-323 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810903514172 En ligne : https://doi.org/10.1080/13658810903514172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30517 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2010041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2010042	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Effects of topographic variability and Lidar sampling density on several DEM interpolation methods / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 6 (June 2010)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 6 (June 2010) . - pp 701 - 712 Titre : Effects of topographic variability and Lidar sampling density on several DEM interpolation methods Type de document : Article/Communication Auteurs : Q. Guo, Auteur ; W. Li, Auteur ; H. Yu, Auteur ; O. Alvarez, Auteur Année de publication : 2010 Article en page(s) : pp 701 - 712 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification barycentrique [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fonction spline d'interpolation [Termes IGN] interpolation [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] krigeage [Termes IGN] modèle numérique de terrain [Termes IGN] Triangulated Irregular Network [Termes IGN] variabilité Résumé : (Auteur) This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability. Numéro de notice : A2010-228 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.6.701 En ligne : https://doi.org/10.14358/PERS.76.6.701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30422 [article]

The role of Web features et and Web Map Services in real-time geospatial data sharing for time-critical applications / C. Zhang in Cartography and Geographic Information Science, vol 32 n° 4 (October 2005)  

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