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A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
[article]
Titre : A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery Type de document : Article/Communication Auteurs : C. Bachmann, Auteur ; M.H. Bettenhausen, Auteur ; R.A. Fusina, Auteur ; et al., Auteur Année de publication : 2003 Article en page(s) : pp 2488 - 2499 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classificateur
[Termes IGN] classification
[Termes IGN] image aérienne
[Termes IGN] image HYMAP
[Termes IGN] image hyperspectrale
[Termes IGN] image PROBE
[Termes IGN] littoral
[Termes IGN] occupation du sol
[Termes IGN] parc naturel
[Termes IGN] Virginie (Etats-Unis)Résumé : (Auteur) A credit assignment approach to decision-based classifier fusion is developed and applied to the problem of land-cover classification from multiseason airborne hyperspectral imagery. For each input sample, the new method uses a smoothed estimated reliability measure (SERM) in the output domain of the classifiers. SERM requires no additional training beyond that needed to optimize the constituent classifiers in the pool, and its generalization (test) accuracy exceeds that of a number of other extant methods for classifier fusion. Hyperspectral imagery from HyMAP and PROBE2 acquired at three points in the growing season over Smith Island, VA, a barrier island in the Nature Conservancy's Virginia Coast Reserve, serves as the basis for comparing SERM with other approaches. Numéro de notice : A2003-318 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818537 En ligne : https://doi.org/10.1109/TGRS.2003.818537 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22614
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) . - pp 2488 - 2499[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03111 RAB Revue Centre de documentation En réserve L003 Disponible Linear features extraction in rain forest context from interferometric SAR images by fusion of coherence and amplitude information / V.P. Onana in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
[article]
Titre : Linear features extraction in rain forest context from interferometric SAR images by fusion of coherence and amplitude information Type de document : Article/Communication Auteurs : V.P. Onana, Auteur ; Emmanuel Trouvé, Auteur ; G. Mauris, Auteur ; Jean-Paul Rudant , Auteur ; E. Tonye, Auteur Année de publication : 2003 Article en page(s) : pp 2540 - 2556 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Cameroun
[Termes IGN] classification barycentrique
[Termes IGN] classification floue
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt tropicale
[Termes IGN] fusion d'images
[Termes IGN] image ERS-SAR
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] littoral
[Termes IGN] mangrove
[Termes IGN] objet géographique linéaireRésumé : (Auteur) This paper presents an almost unsupervised fusion algorithm on linear features (LF) extraction in synthetic aperture radar (SAR) interferometric data, in particular for mangroves/shorelines and thin internal channels. The spatial information on LFs is first extracted in the coherence image, where they are wider and more visible : water regions (in particular thin internal channels) are dark areas (low coherence) due to the temporal decorrelation of backscattering signals in these and surrounding regions, whereas conventional vegetation regions are brighter areas (high coherence). These approximate locations of LFs are further refined by using the edge map coming from a semantic fuzzy fusion of the coefficient of variation (CV) and the ratio of local means (RLM) measured in the amplitude image. The final detection of LFs is then performed by merging the two fuzzy inputs : the spatial information and the edge location map. The membership degree statistics of CV and RLM semantic fusion measures are introduced in order to illustrate the location detection ability. The originality of this method in comparison with conventional approaches is in the fusion scheme that follows the interpreter behavior by using first the coherence image for a fuzzy detection where thin LFs are more visible, but have low location accuracy, and then the amplitude image where they are poorly visible, but with higher location accuracy, to obtain improved results. A quantitative performance evaluation is also presented. The method has been applied on real interferometric SAR images from European Remote Sensing satellites over the western part of Cameroon. Numéro de notice : A2003-321 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818383 En ligne : https://doi.org/10.1109/TGRS.2003.818383 Format de la ressource électronique : URl article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22617
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) . - pp 2540 - 2556[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03111 RAB Revue Centre de documentation En réserve L003 Disponible A Markov random field approach to spatio-temporal contextual image classification / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)
[article]
Titre : A Markov random field approach to spatio-temporal contextual image classification Type de document : Article/Communication Auteurs : F. Melgani, Auteur ; S.B. Serpico, Auteur Année de publication : 2003 Article en page(s) : pp 2478 - 2487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classificateur paramétrique
[Termes IGN] fusion d'images
[Termes IGN] image ERS-SAR
[Termes IGN] image Landsat-TM
[Termes IGN] jeu de données localisées
[Termes IGN] méthode robuste
[Termes IGN] précision de la classificationRésumé : (Auteur) Markov random fields (MRFs) provide a useful and theoretically well-established tool for integrating temporal contextual information into the classification process. In particular, when dealing with a sequence of temporal images, the usual MRF-based approach consists in adopting a "cascade" scheme, i.e., in propagating the temporal information from the current image to the next one of the sequence. The simplicity of the cascade scheme makes it attractive ; on the other hand, it does not fully exploit the temporal information available in a sequence of temporal images. In this paper, a "mutual" MRF approach is proposed that aims at improving both the accuracy and the reliability of the classification process by means of a better exploitation of the temporal information. It involves carrying out a bidirectional exchange of the temporal information between the defined single-time MRF models of consecutive images. A difficult issue related to MRFs is the determination of the MRF model parameters that weight the energy terms related to the available information sources. To solve this problem, we propose a simple and fast method based on the concept of "minimum perturbation" and implemented with the pseudo-inverse technique for the minimization of the sum of squared errors. Experimental results on a multitemporal dataset made up of two multisensor (Landsat Thematic Mapper and European Remote Sensing 1 synthetic aperture radar) images are reported. The results obtained by the proposed "mutual" approach show a clear improvement in terms of classification accuracy over those yielded by a reference MRF-based classifier. The presented method to automatically estimate the MRF parameters yielded significant results that make it an attractive alternative to the usual trial-and-error search procedure. Numéro de notice : A2003-317 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817269 En ligne : https://doi.org/10.1109/TGRS.2003.817269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22613
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003) . - pp 2478 - 2487[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03111 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
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-CarloRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 11 (November 2003)[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03111 RAB Revue Centre de documentation En réserve L003 Disponible Strategies for integrating information from multiple resolutions into land-use/land-cover classification routines / D.M. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 11 (November 2003)
[article]
Titre : Strategies for integrating information from multiple resolutions into land-use/land-cover classification routines Type de document : Article/Communication Auteurs : D.M. Chen, Auteur ; D. Stow, Auteur Année de publication : 2003 Article en page(s) : pp 1279 - 1287 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] banlieue
[Termes IGN] classification barycentrique
[Termes IGN] classification multidimensionnelle
[Termes IGN] image à très haute résolution
[Termes IGN] intégration de données
[Termes IGN] métropole
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] résolution multiple
[Termes IGN] San DiegoRésumé : (Auteur) With the development of new remote sensing systems, very high spatial and spectral resolution images now provide a source for detailed and continuous sampling of the Earth's surface from local to regional scales. This paper presents three strategies for selecting and integrating information from different spatial resolutions into classification routines. One strategy is to combine layers of image varying resolution. A second strategy involves comparing the a posteriori probabilities of each class at different resolutions. Another strategy is based on a top-down approach stating with the coarsest resolution. The multiresolution strategies are tested using simulated multiresolution images for a portion of the rural-urban fringe of the SanDiego Metropolitan Area. The classification accuracy obtained from using three multiple strategies was greater when compared with that from a conventional single-resolution approach. Among the three strategies, the top-down approach resulted in the highest classification accuracy with a Kappa value of 0,648 compared to a Kappa of 0,566 for the conventional classifier. Numéro de notice : A2003-294 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.11.1279 En ligne : https://doi.org/10.14358/PERS.69.11.1279 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22589
in Photogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 11 (November 2003) . - pp 1279 - 1287[article]Bayesian classification by data augmentation / B. Regguzoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkData fusion and feature extraction in the wavelet domain / Magnus Orn Ulfarsson in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkA neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery / E. Binaghi in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkA combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkCurrent GPS data analysis at CLDG for the IGS TIGA Pilot Project / Guy Wöppelmann in Cahiers bleus du Centre Europeen de Geodynamique et de Séismologie, vol 23 (2003)PermalinkDétermination de classes de relief à l'aide de données ERS1 sur des bassins versants tropicaux de Guyane / Marc Lointier in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 172 (Octobre 2003)PermalinkIncreasing the spatial resolution of agricultural land cover maps using a Hopfield neural network / A.J. Tatem in International journal of geographical information science IJGIS, vol 17 n° 7 (october 2003)PermalinkA Markov random field-based approach to decision-level fusion for remote sensing image classification / Ryuei Nishii in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkMultitemporal/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)PermalinkVers une intégration des techniques spatiales pour la gestion des inondations / Jean-Baptiste Henry in Bulletin [Société Française de Photogrammétrie et Télédétection], n° 172 (Octobre 2003)PermalinkMapping of the tropical forest cover of insular Southeast Asia from SPOT-4 Vegetation images / Hans-Jürgen Stibig in International Journal of Remote Sensing IJRS, vol 24 n° 18 (September 2003)PermalinkComparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change / K.C. Seto in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkA comparison of urban mapping methods using high-resolution digital imagery / N. Thomas in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkA hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkICEAGE: interactive clustering and exploration of large and high-dimensional geodata / D. Guo in Geoinformatica, vol 7 n° 3 (September - November 2003)PermalinkImprovements to urban area characterization using multitemporal and multiangle SAR images / F. Dell'acqua in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkImproving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkLand-use and land-cover change, urban heat island phenomenon, and health implications: a remote sensing approach / C.P. Lo in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkMapping urban extent using satellite radar interferometry / W. Grey in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkMeasuring the physical composition of urban morphology using multiple endmember spectral mixture models / T. Rashed in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)Permalink