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Sampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)
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Titre : Sampling piecewise convex unmixing and endmember extraction Type de document : Article/Communication Auteurs : Alina Zare, Auteur ; Paul Garder, Auteur ; George Casella, Auteur Année de publication : 2013 Article en page(s) : pp 1655 - 1665 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du simplexe
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] échantillonnage d'image
[Termes IGN] ensemble convexe
[Termes IGN] image hyperspectrale
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] signature spectraleRésumé : (Auteur) A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing methods, endmembers are generally represented as a single point in the high-dimensional space. However, the spectral signature for a material varies as a function of the inherent variability of the material and environmental conditions. Therefore, it is more appropriate to represent each endmember as a full distribution and use this information during spectral unmixing. The proposed method searches for several sets of endmember distributions. By using several sets of endmember distributions, a piecewise convex mixing model is applied, and given this model, the proposed method performs spectral unmixing and endmember estimation given this nonlinear representation of the data. Each set represents a random simplex. The vertices of the random simplex are modeled by the endmember distributions. The hyperspectral data are partitioned into sets associated with each of the extracted sets of endmember distributions using a Dirichlet process prior. The Dirichlet process prior also estimates the number of sets. Thus, the Metropolis-within-Gibbs sampler partitions the data into convex regions, estimates the required number of convex regions, and estimates endmember distributions and abundance values for all convex regions. Results are presented on real hyperspectral and simulated data that indicate the ability of the method to effectively estimate endmember distributions and the number of sets of endmember distributions. Numéro de notice : A2013-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2207905 En ligne : https://doi.org/10.1109/TGRS.2012.2207905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32272
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 3 Tome 2 (March 2013) . - pp 1655 - 1665[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013031B RAB Revue Centre de documentation En réserve L003 Disponible Topological gradient connection analysis for feature detection / Chao-Yuan Lo in Photogrammetric record, vol 28 n° 141 (March - May 2013)
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Titre : Topological gradient connection analysis for feature detection Type de document : Article/Communication Auteurs : Chao-Yuan Lo, Auteur ; Lieng-Chien Chen, Auteur Année de publication : 2013 Article en page(s) : pp 7 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] connexité (topologie)
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Canny
[Termes IGN] gradient
[Termes IGN] ligne caractéristique
[Termes IGN] lissage de courbe
[Termes IGN] niveau de gris (image)
[Termes IGN] relation topologiqueRésumé : (Auteur) Edges and corners are two major image features in the modelling of man-made objects; an edge provides strong geometric orientation and corners possess good localisation. Feature detection is the basis of image processing for numerous applications such as image registration and object modelling. Completeness and localisation are the two major considerations for these applications; however, illumination, reflectance and shadows may interfere with image grey values to produce various gradients along an edge. Thus, threshold selection is an important step in obtaining suitable features in target-dependent methods as improper selection might cause information loss and broken edges. Instead of threshold selection, this study therefore proposes a feature extraction method using topological gradient connection (TGC) analysis involving three steps: grey value refinement, gradient computation and topological connection analysis. The first step uses a Gaussian filter to smooth the grey value image. The second step computes directional gradients to identify ridge pixels and collect feature candidates. The third step analyses adjacent candidates based on the criterion of topological connection. This three-step tracing procedure combines these connected candidates into a single object. The proposed scheme employs different images derived from various sensors and compares them with the Canny operator (using manually selected thresholds) and manually plotted corners for detection ability assessment. Experimental results indicate that the automatic results are more complete for subtle feature lines than the Canny edges. In addition, the proposed method provides higher flexibility in selecting suitable feature layers for different applications. Numéro de notice : A2013-149 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2012.00703.x Date de publication en ligne : 16/12/2012 En ligne : https://doi.org/10.1111/j.1477-9730.2012.00703.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32287
in Photogrammetric record > vol 28 n° 141 (March - May 2013) . - pp 7 - 26[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2013011 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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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'imageRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 833 - 843[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013021 RAB Revue Centre de documentation En réserve L003 Disponible A graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)
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Titre : A graph-based classification method for hyperspectral images Type de document : Article/Communication Auteurs : J. Bai, Auteur ; S. Xiang, Auteur ; C. Pan, Auteur Année de publication : 2013 Article en page(s) : pp 803 - 817 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme Graph-Cut
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'image
[Termes IGN] signature spectraleRésumé : (Auteur) The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method. Numéro de notice : A2013-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205002 En ligne : https://doi.org/10.1109/TGRS.2012.2205002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32219
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 803 - 817[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013021 RAB Revue Centre de documentation En réserve L003 Disponible Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning / G. Zheng in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)
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Titre : Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning Type de document : Article/Communication Auteurs : G. Zheng, Auteur ; L. Moskal, Auteur ; S.H. Kim, Auteur Année de publication : 2013 Article en page(s) : pp 777 - 787 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] image hémisphérique
[Termes IGN] Leaf Area Index
[Termes IGN] projection azimutale équivalente de lambert
[Termes IGN] projection stéréographique
[Termes IGN] rastérisation
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Terrestrial laser scanner (TLS)-based leaf area index (LAI) retrieval is an appealing concept, due to the ability to capture structural information of canopies as 3D point cloud data (PCD). TLS-based LAI estimation methods promise a nondestructive tool for spatially explicit calibration of LAI estimated by aerial or satellite remote sensing techniques. These methods also overcome the sky condition restrictions of on-ground optical instruments such as hemispherical photography frequently used for LAI estimation. This paper presents a new method for estimating the effective LAI (LAIe) directly from PCD generated by TLS in heterogeneous forests. We converted the 3-D PCD into 2-D raster images, similar to hemispherical photographs, using two geometrical projection techniques in order to estimate gap fraction and LAIe using a linear least squares method. Our results indicated that the TLS-based algorithm was able to capture the variability in LAIe of forest stands with a range of densities. The TLS-based LAIe estimation method explained 89.1% (rmse = 0.01 ; p Numéro de notice : A2013-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205003 En ligne : https://doi.org/10.1109/TGRS.2012.2205003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32218
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 777 - 787[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013021 RAB Revue Centre de documentation En réserve L003 Disponible Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)
PermalinkSpectral material mapping using hyperspectral imagery : a review of spectral matching and library search methods / Sennaraj Vishnu in Geocarto international, vol 28 n° 1-2 (February - May 2013)
PermalinkAppariement entre images de point de vue éloignés par utilisation de carte de profondeur / Narut Soontranon (2013)
PermalinkComparaison et évaluation de méthodes d'extraction automatique d'objets sur des images optique et radar / Charlotte Benedetto (2013)
PermalinkComparison of VHR panchromatic texture features for tillage mapping / Nesrine Chehata (juillet 2013)
PermalinkContribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)
PermalinkCrop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery / B. Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
PermalinkEuroSDR project Commission 1, Radiometric aspects of digital photogrammetric images / Eija Honkavaara (2013)
PermalinkPermalinkA hybrid multiview stereo algorithm for modeling urban scenes / Florent Lafarge in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 35 n° 1 (January 2013)
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