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The IGN CAMv2 system / Jean-Philippe Souchon in Photogrammetric record, vol 25 n° 132 (December 2010 - February 2011)
[article]
Titre : The IGN CAMv2 system Type de document : Article/Communication Auteurs : Jean-Philippe Souchon , Auteur ; Christian Thom , Auteur ; Christophe Meynard , Auteur ; Olivier Martin , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2010 Article en page(s) : pp 402 - 421 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] caméra numérique
[Termes IGN] étalonnage géométrique
[Termes IGN] étalonnage radiométrique
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] instrumentation IGN
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] prise de vue aérienneRésumé : (Auteur) After 10 years of pioneering aerial digital camera development, IGN’s CAMv2 project began in 2006 with the main goal of upgrading the then current system. This had been used for research applications since 1996 in more than six different configurations and in production since 2003 configured with four coloured channels. The new system is still highly modular, enabling combinations of several “basic components”, each consisting of one digital camera head with its storage and control unit. The new camera head was developed around the Kodak KAF-39 000, 39-megapixel array sensor (7216 x 5412 pixels). The system is also very versatile thanks to the possible choice of more than 10 different lenses (from 35 mm up to 180 mm focal length) and interchangeable spectral filters. The mechanical and electronic designs have been reduced in size so as to permit many different configurations including multiple camera heads on the same gyro-stabilised mount. The combination of the performance of camera heads and control and storage units allows a frame rate of one image per 2 s with storage redundancy and 1 s without, leading to a minimum ground sample distance (GSD) of about 9 cm with 60% overlap at an airspeed of 100 m/s. In order to use the new system to best advantage, the adjustment and calibration processes had to be improved. During the summer of 2009, three systems configured for colour imagery, each using four nadir-viewing camera heads (RGB and NIR), were used to acquire images for IGN production work. In January 2009, an eight-camera-head configuration was tested in order to achieve 155-megapixel pan-sharpened images with a pan-sharpening ratio of 2 x 2, and a final swath width of about 14 400 pixels. At that time this configuration needed two windows in the aircraft, but it is now installed on a single, bigger mount. Numéro de notice : A2010-533 Affiliation des auteurs : LOEMI (1985-2011) Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2010.00601.x Date de publication en ligne : 22/12/2010 En ligne : https://doi.org/10.1111/j.1477-9730.2010.00601.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30725
in Photogrammetric record > vol 25 n° 132 (December 2010 - February 2011) . - pp 402 - 421[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2010041 RAB Revue Centre de documentation En réserve L003 Disponible Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones / C. Gomez in Remote sensing of environment, vol 114 n° 11 (15/11/2010)
[article]
Titre : Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones Type de document : Article/Communication Auteurs : C. Gomez, Auteur ; M. Mangeas, Auteur ; Marcel Petit, Auteur ; Christina Corbane, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 2731 - 2744 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] carte thématique
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] Coffea (genre)
[Termes IGN] couvert forestier
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] modèle numérique de surface
[Termes IGN] Nouvelle-Calédonie
[Termes IGN] ombre
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] prédictionRésumé : (Auteur) In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity. Numéro de notice : A2010-402 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2010.06.007 En ligne : https://doi.org/10.1016/j.rse.2010.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30595
in Remote sensing of environment > vol 114 n° 11 (15/11/2010) . - pp 2731 - 2744[article]Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Multiple Spectral–Spatial Classification Approach for Hyperspectral Data Type de document : Article/Communication Auteurs : Yuliya Tarabalka, Auteur ; Jon Atli Benediktsson, Auteur ; Jocelyn Chanussot, Auteur ; James C. Tilton, Auteur Année de publication : 2010 Article en page(s) : pp 4122 - 4132 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification multibande
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) A new multiple-classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region with a corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker-selection procedure, each of them combining the results of a pixelwise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies when compared with previously proposed classification techniques. Numéro de notice : A2010-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2062526 Date de publication en ligne : 13/09/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2062526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30673
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 4122 - 4132[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible vol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing society
[n° ou bulletin]
est un bulletin de IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) (1986 -)
Titre : vol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing Type de document : Périodique Auteurs : Geoscience and remote sensing society, Auteur Année de publication : 2010 Importance : 250 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] compression d'image
[Termes IGN] étalonnage
[Termes IGN] image hyperspectrale
[Termes IGN] traitement d'imageNuméro de notice : 065-201011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique En ligne : http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5607221&punumber=36 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=13574 [n° ou bulletin]Contient
- Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
- Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
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