Centre de documentation
scientifique

Dépouillements

A Bayesian approach to classification of multiresolution remote sensing data / G. Storvik in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 539 - 547 Titre : A Bayesian approach to classification of multiresolution remote sensing data Type de document : Article/Communication Auteurs : G. Storvik, Auteur ; R. Fjortoft, Auteur ; A.H. Schistad, Auteur Année de publication : 2005 Article en page(s) : pp 539 - 547 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multiéchelle [Termes IGN] champ aléatoire de Markov [Termes IGN] classification [Termes IGN] estimation bayesienne [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] limite de résolution géométrique [Termes IGN] modèle de Markov [Termes IGN] résolution multiple Résumé : (Auteur) Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This paper presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Result obtained on simulated and real satellite images are presented. Numéro de notice : A2005-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.841395 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1396326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27305 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Partially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 559 - 570 Titre : Partially supervised classification of remote sensing images through SVM-based probability density estimation Type de document : Article/Communication Auteurs : P. Mantero, Auteur ; G. Moser, Auteur ; S.B. Serpico, Auteur Année de publication : 2005 Article en page(s) : pp 559 - 570 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] estimation statistique [Termes IGN] probabilités [Termes IGN] réalité de terrain Résumé : (Auteur) A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used. Numéro de notice : A2005-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842022 En ligne : https://doi.org/10.1109/TGRS.2004.842022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27306 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A new differential geometric method to rectify digital images of the Earth's surface using isothermal coordinates / M. Karslioglu in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 666 - 672 Titre : A new differential geometric method to rectify digital images of the Earth's surface using isothermal coordinates Type de document : Article/Communication Auteurs : M. Karslioglu, Auteur ; J. Freidrich, Auteur Année de publication : 2005 Article en page(s) : pp 666 - 672 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Ankara (Turquie) [Termes IGN] carte photographique [Termes IGN] ellipsoïde (géodésie) [Termes IGN] orthoimage [Termes IGN] projection orthographique [Termes IGN] redressement d'image [Termes IGN] redressement différentiel [Termes IGN] World Geodetic System 1984 Résumé : (Auteur) A new method to rectify monoscopic digital images and generate orthoimages of the earth's surface is described. It replaces the standard procedure, which transfers the perspective projection of a frame photograph to an orthographic projection of pixels onto a reference plane using corresponding corrections. Instead, the perspective forward projection is kept but every pixel is vertically mapped along the surface normal onto a curved reference surface, for example, the ellipsoid of the World Geodetic System 1984 under the condition that a precise enough surface elevation model is available. The gained ellipsoidal coordinates (latitude, longitude and height) of each pixel are then transformed into isothermal coordinates like the Universal Transverse Mercator coordinates. Their differential geometric characteristics allow mapping every pixel to a reference plane producing, after some interpolation between irregularly spaced pixels, a photomap with the same geometric properties as any other topographic map. The suitability of the method is demonstrated by two photomaps from Ankara, Turkey, which are compared to high-quality topographic maps whereby the average position errors are about 2-3 pixels. Numéro de notice : A2005-169 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842483 En ligne : https://doi.org/10.1109/TGRS.2004.842483 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27307 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible