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De-shadowing of satellite/airborne imagery / R. Richter in International Journal of Remote Sensing IJRS, vol 26 n° 15 (August 2005)
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[article]
Titre : De-shadowing of satellite/airborne imagery Type de document : Article/Communication Auteurs : R. Richter, Auteur ; A. Muller, Auteur Année de publication : 2005 Article en page(s) : pp 3137 - 3148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande visible
[Termes IGN] correction des ombres
[Termes IGN] filtre numérique
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image spatiale
[Termes IGN] matrice de covariance
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] seuillage d'imageRésumé : (Auteur) A de-shadowing technique is presented for multispectral and hyperspectral imagery over land acquired by satellite/airborne sensors. The method requires a channel in the visible and at least one spectral band in the near-infrared (0.8-1um) region, but performs much better if bands in the short-wave infrared region (around 1.6 and 2.2 um) are available as well. The algorithm consists of these major components: (i) calculation of the covariance matrix and zero-reflectance matched filter vector, (ii) derivation of the unsealed and scaled shadow function, (iii) histogram thresholding of the unscaled shadow function to define the core shadow areas, (iv) region growing to include the surroundings of the core shadow areas for a smooth shadowlclear transition, and (v) de-shadowing of the pixels in the final shadow mask. The critical parameters of the method are discussed. Example images from different climates and landscapes are presented to demonstrate the successful performance of the shadow removal process over land surfaces. Numéro de notice : A2005-323 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500114664 En ligne : https://doi.org/10.1080/01431160500114664 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27459
in International Journal of Remote Sensing IJRS > vol 26 n° 15 (August 2005) . - pp 3137 - 3148[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05151 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling / F. Samadzadegan in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 5 (August - October 2005)
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Titre : Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling Type de document : Article/Communication Auteurs : F. Samadzadegan, Auteur ; A. Azizi, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 255 - 277 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] Allemagne
[Termes IGN] image aérienne
[Termes IGN] image en couleur
[Termes IGN] milieu urbain
[Termes IGN] raisonnement flou
[Termes IGN] reconnaissance automatique
[Termes IGN] reconnaissance d'objets
[Termes IGN] reconstruction 3D
[Termes IGN] réseau neuronal artificiel
[Termes IGN] visualisation 3DRésumé : (Auteur) Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper, a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany. Copyright ISPRS Numéro de notice : A2005-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.02.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.02.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27487
in ISPRS Journal of photogrammetry and remote sensing > vol 59 n° 5 (August - October 2005) . - pp 255 - 277[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-05031 SL Revue Centre de documentation Revues en salle Disponible
[article]
Titre : Clever imaging with Smartscan Type de document : Article/Communication Auteurs : V. Tchernykh, Auteur ; S. Dyblenko, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 41 - 45 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] capteur en peigne
[Termes IGN] capteur imageur
[Termes IGN] distorsion d'image
[Termes IGN] image hyperspectrale
[Termes IGN] numériseur à balayage
[Termes IGN] orientation du capteur
[Termes IGN] satellite d'observation de la Terre
[Termes IGN] temps réelRésumé : (Auteur) The cameras commonly used for Earth observation from satellites require high attitude stability during the image acquisition. For some types of cameras (high-resolution 'pushbroom' scanners in particular), instantaneous attitude changes of even less than one arcsecond result in significant image distortion and blurring. Especially problematic are the effects of high-frequency attitude variations originating from micro-shocks and vibrations produced by the momentum and reaction wheels, mechanically activated coolers, and steering and deployment mechanisms on board. The resulting high attitude-stability requirements for Earth-observation satellites are one of the main reasons for their complexity and high cost. The novel SmartScan imaging concept, based on an opto-electronic system with no moving parts, offers the promise of high-quality imaging with only moderate satellite attitude stability. SmartScan uses real-time recording of the actual image motion in the focal plane of the camera during frame acquisition to correct the distortions in the image. Exceptional real-time performances with subpixel-accuracy image-motion measurement are provided by an innovative high-speed onboard opto-electronic correlation processor. SmartScan will therefore allow pushbroom scanners to be used for hyperspectral imaging from satellites and other space platforms not primarily intended for imaging missions, such as micro- and nano-satellites with simplified attitude control, low-orbiting communications satellites, and manned space stations. Numéro de notice : A2005-404 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : http://www.esa.int/esapub/bulletin/bulletin123/bul123f_tchernykh.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27540
in ESA bulletin > n° 123 (August 2005) . - pp 41 - 45[article]A statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)
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Titre : A statistical self-organizing learning system for remote sensing classification Type de document : Article/Communication Auteurs : H.M. Chi, Auteur ; O.K. Ersoy, Auteur Année de publication : 2005 Article en page(s) : pp 1890 - 1900 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] méthode des moindres carrés
[Termes IGN] noeud
[Termes IGN] système expert
[Termes IGN] transformation non linéaireRésumé : (Auteur) A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality. Numéro de notice : A2005-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.851188 En ligne : https://doi.org/10.1109/TGRS.2005.851188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27529
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 8 (August 2005) . - pp 1890 - 1900[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05081 RAB Revue Centre de documentation En réserve L003 Disponible Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty / Arko Lucieer in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)
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Titre : Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty Type de document : Article/Communication Auteurs : Arko Lucieer, Auteur ; Alfred Stein, Auteur ; Peter F. Fisher, Auteur Année de publication : 2005 Article en page(s) : pp 2917 - 2936 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] extraction automatique
[Termes IGN] image CASI
[Termes IGN] image multibande
[Termes IGN] incertitude des données
[Termes IGN] niveau de gris (image)
[Termes IGN] objet géographique
[Termes IGN] segmentation d'image
[Termes IGN] texture d'imageRésumé : (Auteur) In this study, a segmentation procedure is proposed, based on grey-level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identifiy homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey-scale and colour textures, depicted by accuracy values of 96% and 98% respectively. The second case study involved segmentation of coastal land cover objects from a multispectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP-based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the multivariate LBP texture model in combinaison with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty. Numéro de notice : A2005-294 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500057723 En ligne : https://doi.org/10.1080/01431160500057723 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27430
in International Journal of Remote Sensing IJRS > vol 26 n° 14 (July 2005) . - pp 2917 - 2936[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05141 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Application of multi-temporal high-resolution imagery GPS in a study of the motion of a canyon rim landslide / John Chadwick in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 4 (June - July 2005)
PermalinkA comparative analysis of image fusion methods / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 43 n° 6 (June 2005)
PermalinkA photogrammetric method for single image orientation and measurement / Antonio Maria Garcia Tommaselli in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 6 (June 2005)
PermalinkDesigning fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images / Nikhil R. Pal in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)
PermalinkNeural network model for standard PCA and its variants applied to remote sensing / S. Chitroub in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)
PermalinkSatellite remote sensing for detailed landslide inventories using change detection and image fusion / J. Nichol in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)
PermalinkA robust technique for precise registration of radar and optical satellite images / T.D. Hong in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 5 (May 2005)
PermalinkLand covers change detection at coarse spatial scales based on iterative estimation and previous state information / Sylvie Le Hégarat-Mascle in Remote sensing of environment, vol 95 n° 4 (30/04/2005)
PermalinkA comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification / C.W. Emerson in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)
PermalinkA whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra / E.W. Ramsey in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)
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