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Automatic satellite image georeferencing using a contour-matching approach / Francisco Eugenio in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2869 - 2880 Titre : Automatic satellite image georeferencing using a contour-matching approach Type de document : Article/Communication Auteurs : Francisco Eugenio, Auteur ; F. Marques-, Auteur Année de publication : 2003 Article en page(s) : pp 2869 - 2880 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] contour [Termes IGN] correction géométrique [Termes IGN] détection de contours [Termes IGN] extraction automatique [Termes IGN] géoréférencement [Termes IGN] image multicapteur [Termes IGN] image multitemporelle [Termes IGN] image NOAA-AVHRR [Termes IGN] image Seawifs Résumé : (Auteur) Multitemporal and multisatellite studies or comparisons between satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images. This paper presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows correcting the low-frequency error component, mainly due to timing and orbital model errors, as well as the high-frequency error component, due to variations in the spacecraft's attitude. The approach aims at exploiting the maximum reliable information in the image to guide the matching algorithm. The contour-matching process has three main steps: 1) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; 2) initialization of the contours positions; 3) estimation of the transformation parameters (affine model) using a contour optimization approach. Three different robust and automatic algorithms are proposed for optimization, and their main features are discussed. Finally, the performance of the three proposed algorithms is assessed using a new error estimation technique applied to Advanced Very High Resolution Radiometer (AVHRR), Sea-viewing Wide Field of view Sensor (SeaWiFS), and multisensor AVHRR-SeaWiFS imagery. Numéro de notice : A2003-382 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817226 En ligne : https://ieeexplore.ieee.org/document/1260624 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26462 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Fast SAR image restoration, segmentation, and detection of high-reflectance regions / E. Bratsolis in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2890 - 2899 Titre : Fast SAR image restoration, segmentation, and detection of high-reflectance regions Type de document : Article/Communication Auteurs : E. Bratsolis, Auteur ; M. Sigelle, Auteur Année de publication : 2003 Article en page(s) : pp 2890 - 2899 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] champ aléatoire de Markov [Termes IGN] chatoiement [Termes IGN] classification [Termes IGN] filtre numérique [Termes IGN] histogramme [Termes IGN] image ERS-SAR [Termes IGN] itération [Termes IGN] réflectance [Termes IGN] restauration d'image [Termes IGN] segmentation d'image Résumé : (Auteur) An iterative filter that can be used for speckle reduction and restoration of synthetic aperture radar (SAR) images is presented here. This method can be considered as a first step in the extraction of other important information. The second step is the detection of high-reflectance regions and continues with the segmentation of the total image. We have worked in three-look simulated and real European Remote Sensing 1 satellite amplitude images. The iterative filter is based on a membrane model Markov random field approximation optimized by a synchronous local iterative method. The final form of restoration gives a total sum-preserving regularization for the pixel values of our image. The high-reflectance regions are defined as the brightest regions of the restored image. After the separation of this extreme class, we give a fast segmentation method using the histogram of the restored image. Numéro de notice : A2003-383 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817222 En ligne : https://doi.org/10.1109/TGRS.2003.817222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26463 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Segmentation of remotely sensed images using wavelet and their evaluation in soft computing framework / M. Acharyya in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2900 - 2905 Titre : Segmentation of remotely sensed images using wavelet and their evaluation in soft computing framework Type de document : Article/Communication Auteurs : M. Acharyya, Auteur ; M.K. Kundu, Auteur ; K., De Rajat, Auteur Année de publication : 2003 Article en page(s) : pp 2900 - 2905 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image IRS [Termes IGN] image SPOT [Termes IGN] intelligence artificielle [Termes IGN] ondelette [Termes IGN] recouvrement d'images [Termes IGN] segmentation d'image [Termes IGN] télédétection spatiale Résumé : (Auteur) The present paper describes a feature extraction method based on M-band wavelet packet frames for segmenting remotely sensed images. These wavelet features are then evaluated and selected using an efficient neurofuzzy algorithm. Both the feature extraction and neurofuzzy feature evaluation methods are unsupervised, and they do not require the knowledge of the number and distribution of classes corresponding to various land covers in remotely sensed images. The effectiveness of the methodology is demonstrated on two four-band Indian Remote Sensing 1A satellite (IRS-1A) images containing five to six overlapping classes and a three-band SPOT image containing seven overlapping classes. Numéro de notice : A2003-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815398 En ligne : https://doi.org/10.1109/TGRS.2003.815398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26464 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A cognitive pyramid for contextual classification of remote sensing images / E. Binaghi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 12 (December 2003) . - pp 2906 - 2922 Titre : A cognitive pyramid for contextual classification of remote sensing images Type de document : Article/Communication Auteurs : E. Binaghi, Auteur ; I. Gallo, Auteur ; M. Pepe, Auteur Année de publication : 2003 Article en page(s) : pp 2906 - 2922 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification contextuelle [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] extraction automatique [Termes IGN] image aérienne [Termes IGN] image panchromatique [Termes IGN] image satellite [Termes IGN] Perceptron multicouche [Termes IGN] reconnaissance d'objets [Termes IGN] résolution multiple Résumé : (Auteur) Many cases of remote sensing classification present complicated patterns that cannot he identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels. However, the use of contextual classification is still limited by critical issues, such as complexity and problem dependency. We propose here a contextual classification strategy for object recognition in remote sensing images in an attempt to solve recognition tasks operatively. The salient characteristics of the strategy are the definition of a multiresolution feature extraction procedure exploiting human perception and the use of soft neural classification based on the multilayer perceptron model. Three experiments were conducted to evaluate the performance of the methodology, one in an easily controlled domain using synthetic images, the other two in real domains involving builtup pattern recognition in panchromatic aerial photographs and high-resolution satellite images. Numéro de notice : A2003-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815409 En ligne : https://doi.org/10.1109/TGRS.2003.815409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26465 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03121	RAB	Revue	Centre de documentation	En réserve L003	Disponible