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A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images / P. Lombardo in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) Titre : A new maximum-likelihood joint segmentation technique for multitemporal SAR and multiband optical images Type de document : Article/Communication Auteurs : P. Lombardo, Auteur ; C.J. Oliver, Auteur ; T.M. Pellizzeri, Auteur ; et al., Auteur Année de publication : 2003 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification non dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] fusion d'images [Termes IGN] image ERS-SAR [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] méthode de Monte-Carlo Résumé : (Auteur) In this paper, we devise a new technique for the fusion of a sequence of multitemporal single-channel synthetic aperture radar (SAR) images of a given area with a single multiband optical image. Unlike for SAR, the availability of optical images is largely affected by atmospheric conditions, so that this is a case of practical interest. First, a statistical model for the joint distribution of SAR and optical data is provided. Then, a split-merge test based on this model is derived, and its performance is evaluated both analytically and using a Monte Carlo simulation. A new segmentation technique is introduced (OPT MUM), based on the test and on a region-growing scheme. The effectiveness of the proposed technique for the fusion of multitemporal SAR and multiband optical images is tested on synthetic and real images. Results show that the proposed scheme allows to both 1) discriminate characteristics that would be impossible to distinguish using only a single sensor and 2) increase the overall discrimination performance, even when each sensor has its own discrimination capability. Numéro de notice : A2003-319 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818814 En ligne : https://doi.org/10.1109/TGRS.2003.818814 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22615 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach / T. Macri Pellizzei in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 10 (October 2003) . - pp 2338 - 2353 Titre : Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach Type de document : Article/Communication Auteurs : T. Macri Pellizzei, Auteur ; Paolo Gamba, Auteur ; P. Lombardo, Auteur ; F. Dell'acqua, Auteur Année de publication : 2003 Article en page(s) : pp 2338 - 2353 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] image radar moirée [Termes IGN] image SIR-C [Termes IGN] milieu urbain [Termes IGN] réalité de terrain [Termes IGN] segmentation d'image [Termes IGN] test de performance Résumé : (Auteur) In this paper, we derive two techniques for the classification of Multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium - to - scale features, like the boundaries between urban and non-urban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterise their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analysed by comparing them to the ground truth. Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neuro-fuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information. Numéro de notice : A2003-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818762 En ligne : https://doi.org/10.1109/TGRS.2003.818762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26436 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03101	RAB	Revue	Centre de documentation	En réserve L003	Disponible

vol 58 n° 1-2 - June - December 2003 - Algorithms and techniques for multi-source data fusion in urban areas (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Paolo Gamba  

Public [n° ou bulletin]  est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) (1990 -)   Titre : vol 58 n° 1-2 - June - December 2003 - Algorithms and techniques for multi-source data fusion in urban areas Type de document : Périodique Auteurs : Paolo Gamba, Éditeur scientifique ; P. Lombardo, Éditeur scientifique ; International society for photogrammetry and remote sensing (1980 -), Auteur Année de publication : 2003 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] conflation [Termes IGN] fusion de données multisource [Termes IGN] milieu urbain [Termes IGN] photogrammétrie numérique [Termes IGN] reconstruction 3D du bâti Numéro de notice : 081-0303 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique En ligne : http://www.sciencedirect.com/science/journal/09242716/58/1-2 Format de la ressource électronique : URL Sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=20812 [n° ou bulletin]

Contient

• Fusion of hyperspectral and radar data using the IHS transformation to enhance urban surface features / C.M. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)  
• Satellite multi-sensor data analysis of urban surface temperatures and Landcover / B. Dousset in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)  
• Detection of building outlines based on the fusion of SAR and optical features / Florence Tupin in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)  
• Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data / Karl Segl in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)  
• Potential and limits of InSAR data for building reconstruction in built-up area / Uwe Stilla in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)  

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-03032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-03031	RAB	Revue	Centre de documentation	En réserve L003	Disponible