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Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation / L. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 97 (November 2014) . - pp 123 – 137 Titre : Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation Type de document : Article/Communication Auteurs : L. Wang, Auteur ; Siyuan Hao, Auteur ; Q. Wang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 123 – 137 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification semi-dirigée [Termes IGN] filtre de Gabor [Termes IGN] graphe [Termes IGN] image hyperspectrale Résumé : (Auteur) Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial distribution of the data. In this paper, a new classification methodology based on the spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Gaussian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., Support Vector Machine, SVM). Experiments on four hyperspectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and the Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM). Numéro de notice : A2014-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.08.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74140 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Modeling the scale dependences of topological relations between lines and regions induced by reduction of attributes / S. Du in International journal of geographical information science IJGIS, vol 24 n°11-12 (december 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n°11-12 (december 2010) . - pp 1649 - 1686 Titre : Modeling the scale dependences of topological relations between lines and regions induced by reduction of attributes Type de document : Article/Communication Auteurs : S. Du, Auteur ; Q. Wang, Auteur ; Luo Guo, Auteur Année de publication : 2010 Article en page(s) : pp 1649 - 1686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse multiéchelle [Termes IGN] attribut [Termes IGN] facteur d'échelle [Termes IGN] généralisation cartographique automatisée [Termes IGN] graphe [Termes IGN] objet géographique linéaire [Termes IGN] objet géographique zonal [Termes IGN] relation topologique Résumé : (Auteur) The scale dependences of topological relations are caused by the changes of spatial objects at different scales, which are induced by the reduction of attributes. Generally, the detailed partitions and multi-scale attributes are stored in spatial databases, while the coarse partitions are not. Consequently, the detailed topological relations can be computed and regarded as known information, while the coarse relations stay unknown. However, many applications (e.g., multi-scale spatial data query) need to deal with the topological relations at multiple scales. In this study new methods are proposed to model and derive the scale dependences of topological relations between lines and multi-scale region partitions. The scale dependences of topological relations are modeled and used to derive the relations between lines and coarse partitions from the relations about the detailed partitions. The derivation can be performed in two steps. At the first step, the topological dependences between a line and two meeting, covered and contained regions are computed and stored into composition tables, respectively. At the second step, a graph is used to represent the neighboring relations among the regions in a detailed partition. The scale dependences and detailed relations are then used to derive topological relations at the coarse level. Our methods can also be extended to handle the scale dependences of relations about disconnected regions, or the combinations of connected and disconnected regions. Because our methods use the scale dependences to derive relations at the coarse level, rather than generating coarse partition and computing the relations with geometric information, they are more efficient to support scale-dependent applications. Numéro de notice : A2010-547 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658811003591672 En ligne : https://doi.org/10.1080/13658811003591672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30739 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2010071	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2010072	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries / S. Du in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 6 (November - December 2008)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 63 n° 6 (November - December 2008) . - pp 593 - 609 Titre : Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries Type de document : Article/Communication Auteurs : S. Du, Auteur ; Luo Guo, Auteur ; Q. Wang, Auteur ; Q. Qin, Auteur Année de publication : 2008 Article en page(s) : pp 593 - 609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de données localisées [Termes IGN] limite indéterminée [Termes IGN] matrice [Termes IGN] objet géographique complexe [Termes IGN] relation topologique [Termes IGN] représentation par frontière surfacique [Termes IGN] structure hiérarchique de données [Termes IGN] système d'information géographique Résumé : (Auteur) The extended 9-intersection matrix is used to formalize topological relations between uncertain regions while it is designed to satisfy the requirements at a concept level, and to deal with the complex regions with broad boundaries (CBBRs) as a whole without considering their hierarchical structures. In contrast to simple regions with broad boundaries, CBBRs have complex hierarchical structures. Therefore, it is necessary to take into account the complex hierarchical structure and to represent the topological relations between all regions in CBBRs as a relation matrix, rather than using the extended 9-intersection matrix to determine topological relations. In this study, a tree model is first used to represent the intrinsic configuration of CBBRs hierarchically. Then, the reasoning tables are presented for deriving topological relations between child, parent and sibling regions from the relations between two given regions in CBBRs. Finally, based on the reasoning, efficient methods are proposed to compute and derive the topological relation matrix. The proposed methods can be incorporated into spatial databases to facilitate geometric-oriented applications. Copyright ISPRS Numéro de notice : A2008-433 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.02.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29502 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-08061	SL	Revue	Centre de documentation	Revues en salle	Disponible

Land-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network / H. Bagan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 3 (March 2008) . - pp 333 - 342 Titre : Land-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network Type de document : Article/Communication Auteurs : H. Bagan, Auteur ; Q. Wang, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 333 - 342 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de Kohonen [Termes IGN] classification par réseau neuronal [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image Terra-ASTER [Termes IGN] occupation du sol [Termes IGN] précision de la classification Résumé : (Auteur) In this study, we developed a land-cover classification methodology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) band combinations based on wavelet fusion and the selforganizing map (SOM) neural network methods, and compared the classification accuracies of different combinations of ASTER multi-band data. A wavelet fusion concept named ARSIS (Amélioration de la Résolution Spatiale par Injection de Structures) was used to fuse ASTER data in the preprocessing stage. In order to apply the wavelet fusion method to ASTER data, the principal components of ASTER VNIR data were computed. The first principal component was used as the base image for wavelet fusion. In our experiments, the spatial resolution of ASTER VNIR, SWIR, and TIR data was adjusted to the same 15 m. SOM classification accuracy was increased from 83 percent to 93 percent by this fusion, and classification accuracy increased along with the increase of band numbers. Classification accuracy reaches the highest value when all 14 bands are used, but classification accuracy closely approached the highest value when three VNIR bands, three SWIR bands, and two TIR bands were used. A similar tendency was also obtained by the maximum likelihood classification (MLC) method, but the classification accuracies of MLC over all band combinations were considerably obviously lower than those obtained by the SOM method. Copyright ASPRS Numéro de notice : A2008-075 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.3.333 En ligne : https://doi.org/10.14358/PERS.74.3.333 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29070 [article]

Radar backscatter measurements over saline ice / S. Gogineni in International Journal of Remote Sensing IJRS, vol 11 n° 4 (April 1990)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 11 n° 4 (April 1990) . - pp 603 - 615 Titre : Radar backscatter measurements over saline ice Type de document : Article/Communication Auteurs : S. Gogineni, Auteur ; R.K. Moore, Auteur ; Q. Wang, Auteur ; A. Gow, Auteur ; R.G. Onstott, Auteur Année de publication : 1990 Article en page(s) : pp 603 - 615 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] banquise [Termes IGN] glace [Termes IGN] polarisation [Termes IGN] radar [Termes IGN] rétrodiffusion [Termes IGN] sel Numéro de notice : A1990-072 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431169008955044 En ligne : https://doi.org/10.1080/01431169008955044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25490 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-90041	RAB	Revue	Centre de documentation	En réserve L003	Disponible