Centre de documentation
scientifique

Dépouillements

Spectral resolution requirements for mapping urban areas / Martin Herold in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1907 - 1919 Titre : Spectral resolution requirements for mapping urban areas Type de document : Article/Communication Auteurs : Martin Herold, Auteur ; M.E. Gardner, Auteur ; D.A. Roberts, Auteur Année de publication : 2003 Article en page(s) : pp 1907 - 1919 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] cartographie urbaine [Termes IGN] classification [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image Ikonos [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] image optique [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] précision de la classification Résumé : (Auteur) This study evaluated how spectral resolution of spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high withinclass variability. Numéro de notice : A2003-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815238 En ligne : https://doi.org/10.1109/TGRS.2003.815238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22543 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1920 - 1932 Titre : A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas Type de document : Article/Communication Auteurs : A.K. Shackelford, Auteur ; C.H. Davis, Auteur Année de publication : 2003 Article en page(s) : pp 1920 - 1932 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification floue [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] milieu urbain [Termes IGN] texture d'image Résumé : (Auteur) In this paper, we investigate the usefulness of high-resolution multispectral satellite imagery for classification of urban and suburban areas and present a fuzzy logic methodology to improve classification accuracy. Panchromatic and multispectral IKONOS image datasets are analyzed for two urban locations in this study. Both multispectral and pansharpened multispectral images are first classified using a traditional maximum-likelihood approach. Maximum-likelihood classification accuracies between 79 % to 87 % were achieved with significant misclassification error between the spectrally similar Road and Building urban land cover types. A number of different texture measures were investigated, and a length-width contextual measure is developed. These spatial measures were used to increase the discrimination between spectrally similar classes, thereby yielding higher accuracy urban land cover maps. Finally, a hierarchical fuzzy classification approach that makes use of both spectral and spatial information is presented. This technique is shown to increase the discrimination between spectrally similar urban land cover classes and results in classification accuracies that are 8 % to I I% larger than those from the traditional maximumlikelihood approach. Numéro de notice : A2003-249 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814627 En ligne : https://doi.org/10.1109/TGRS.2003.814627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22544 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands / R. Dekker in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1950 - 1958 Titre : Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands Type de document : Article/Communication Auteurs : R. Dekker, Auteur Année de publication : 2003 Article en page(s) : pp 1950 - 1958 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] 1:250.000 [Termes IGN] analyse texturale [Termes IGN] cartographie urbaine [Termes IGN] classification [Termes IGN] histogramme [Termes IGN] image ERS-SAR [Termes IGN] image radar [Termes IGN] milieu urbain [Termes IGN] mise à jour cartographique [Termes IGN] Pays-Bas [Termes IGN] Rotterdam (Pays-Bas) [Termes IGN] variogramme Résumé : (Auteur) In single-band and single-polarized synthetic aperture radar (SAR) image classification, texture holds useful information. In a study to assess the map-updating capabilities of such sensors in urban areas, some modern texture measures were investigated. Among them were histogram measures, wavelet energy, fractal dimension, lacunarity, and semivariograms. The latter were chosen as an alternative for the well-known gray-level cooccurrence family of features. The area that was studied using a European Remote Sensing Satellite 1(ERS1) SAR image was the conurbation around Rotterdam and The Hague in The Netherlands. The area can be characterized as a well-planned dispersed urban area with residential areas, industry, greenhouses, pasture, arable land, and some forest. The digital map to be updated was a 1: 250 000 Vector Map (VMapl). The study was done on the basis of non-parametric separability measures and classification techniques because most texture distributions were not normal. The conclusion is that texture improves the classification accuracy. The measures that performed best were mean intensity (actually no texture), variance, weighted-rank fill ratio, and semivariogram, but the accuracies vary for different classes. Despite the improvement, the overall classification accuracy indicates that the land-cover information content of ERS1 leaves something to be desired. Numéro de notice : A2003-250 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814628 En ligne : https://doi.org/10.1109/TGRS.2003.814628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22545 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Improvements 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1996 - 2004 Titre : Improvements to urban area characterization using multitemporal and multiangle SAR images Type de document : Article/Communication Auteurs : F. Dell'acqua, Auteur ; Paolo Gamba, Auteur ; G. Lisini, Auteur Année de publication : 2003 Article en page(s) : pp 1996 - 2004 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] bande C [Termes IGN] bande X [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] extraction du réseau routier [Termes IGN] histogramme [Termes IGN] image ERS-SAR [Termes IGN] image multitemporelle [Termes IGN] Lombardie [Termes IGN] milieu urbain [Termes IGN] précision de la classification [Termes IGN] réalité de terrain [Termes IGN] réseau routier Résumé : (Auteur) In this paper, we present some improvements to urban area characterization by means of synthetic aperture radar (SAR) images using multitemporal and multiangle datasets. The first aim of this research is to show that a temporal sequence of satellite SAR data may improve the classification accuracy and the discriminability of land cover classes in an urban area. Similarly, a second point worth discussing is to what extent multiangle SAR data allows extracting complementary urban features, exploiting different acquisition geometries. To these aims, in this paper, we show results on the same urban test site (Pavia, northern Italy), referring to a sequence of European Remote Sensing Satellite 1/2 (ERS1/2) Cband images and to a set of simulated Xband data with a finer spatial resolution and different viewing angles. In particular, the multitemporal data is analyzed by means of a novel procedure based on a neurofuzzy classifier whose input is a subset of the ERS sequence chosen using the histogram distance index. Instead, the multiangle dataset is used to provide a better characterization of the road network in the area, overcoming effects due to the orientation of the SAR sensor. Numéro de notice : A2003-251 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814631 En ligne : https://doi.org/10.1109/TGRS.2003.814631 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22546 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Surface soil moisture retrieval from L-band radiometry: a global regression study / T. Pellarin in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2037 - 2051 Titre : Surface soil moisture retrieval from L-band radiometry: a global regression study Type de document : Article/Communication Auteurs : T. Pellarin, Auteur ; J.C. Calvet, Auteur ; Jean-Pierre Wigneron, Auteur Année de publication : 2003 Article en page(s) : pp 2037 - 2051 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande L [Termes IGN] humidité du sol [Termes IGN] luminance lumineuse [Termes IGN] radiométrie [Termes IGN] régression [Termes IGN] température de surface [Termes IGN] zone humide Résumé : (Auteur) Using a global simulation of L-band (1.4 GHz) brightness temperature (TB) for two years (1987 and 1988), the relationship between L(band brightness temperatures and surface soil moisture was analyzed using simple regression models. The global TB dataset describes continental pixels at a halfdegree spatial resolution and accounts for withinpixel heterogeneity, based on 1-km resolution land cover maps. Two different statistical methods were investigated. First, a single regression model was obtained using a linear combination of TB indexes. This method consisted in retrieving surface soil moisture using the same global regression model for all the pixels. Second, a regression model was calibrated over each pixel using similar linear combinations of the TB indexes. In both cases, the influence of the radiometric noise on TB was investigated. Applying these two methods, the capability ,of L-band TB observations to monitor surface soil moisture vas evaluated at the global scale and during a two-year time period. Global maps of the estimated accuracy of the soil moisture retrievals were produced. These results contribute to better define the potential of the observations from future spaceborne missions,such as the Soil Moisture and Ocean Salinity (SMOS) mission Numéro de notice : A2003-252 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.813492 En ligne : https://doi.org/10.1109/TGRS.2003.813492 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22547 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

DTM extraction of Lidar returns via adaptive processing / H.S. Lee in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2063 - 2069 Titre : DTM extraction of Lidar returns via adaptive processing Type de document : Article/Communication Auteurs : H.S. Lee, Auteur ; N.H. Younan, Auteur Année de publication : 2003 Article en page(s) : pp 2063 - 2069 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] données lidar [Termes IGN] extraction automatique [Termes IGN] forêt [Termes IGN] modèle numérique de terrain [Termes IGN] prévision linéaire [Termes IGN] processus Résumé : (Auteur) Airborne light detection and ranging is emerging as a tool to provide accurate digital terrain models (DTMs) of forest areas, since it can penetrate beneath the canopy. Although traditional techniques, such as linear prediction, have shown to be robust type methods for the extraction of DTMs, they fail to effectively model terrain with steep slopes and large variability. In this paper, a modified linear prediction technique, followed by adaptive processing and refinement, is developed. A comparison with the traditional linear prediction method is provided along with statistical measures to ascertain the validity of the foregoing technique. Numéro de notice : A2003-253 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.813849 En ligne : https://doi.org/10.1109/TGRS.2003.813849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22548 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Improving 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2101 - 2112 Titre : Improving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars Type de document : Article/Communication Auteurs : C. Bachmann, Auteur Année de publication : 2003 Article en page(s) : pp 2101 - 2112 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur [Termes IGN] échantillonnage d'image [Termes IGN] erreur de classification [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] optimisation (mathématiques) Résumé : (Auteur) In the past, "active learning" strategies have been proposed for improving the convergence and accuracy of statistical classifiers. However, many of these approaches have large storage requirements or unnecessarily large computational burdens and, therefore, have been impractical for the largescale databases typically, found in remote sensing, especially hyperspectral applications. In this paper, we develop a practical online approach with only modest storage requirements. The new approach improves the convergence rate associated with the optimization of adaptive classifiers, especially in highdimensional remote sensing data. We demonstrate the new approach using PROBE2 hyperspectral imagery and find convergence time improvements of two orders of magnitude in the optimization of landcover classifiers. Numéro de notice : A2003-254 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817207 En ligne : https://doi.org/10.1109/TGRS.2003.817207 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22549 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The use of fully polarimetric information for the fuzzy neural classification of SAR images / C.T. Chen in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2089 - 2100 Titre : The use of fully polarimetric information for the fuzzy neural classification of SAR images Type de document : Article/Communication Auteurs : C.T. Chen, Auteur ; K.S. Chen, Auteur ; Jong-Sen Lee, Auteur Année de publication : 2003 Article en page(s) : pp 2089 - 2100 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] données polarimétriques [Termes IGN] image AIRSAR [Termes IGN] matrice de covariance [Termes IGN] rétrodiffusion [Termes IGN] utilisation du sol [Termes IGN] vectorisation Résumé : (Auteur) This paper presents a method, based on a fuzzy neural network, that uses fully polarimetric information for terrain and land-use classification of synthetic aperture radar (SAR) image. The proposed approach makes use of statistical properties of polarimetric data, and takes advantage of a fuzzy neural network. A distance measure, based on a complex Wishart distribution, is applied using the fuzzy c-means clustering algorithm, and the clustering result is then incorporated into the neural network. Instead of preselecting the polarization channels to form a feature vector, all elements of the polarimetric covariance matrix serve as the target feature vector as inputs to the neural network. It is thus expected that the neural network will include fully polarimetric backscattering information for image classification. With the generalization, adaptation, and other capabilities of the neural network, information contained in the covariance matrix, such as the amplitude, the phase difference, the degree of polarization, etc., can be fully explored. A test image, acquired by the Jet Propulsion Laboratory Airborne SAR (AIRSAR) system, is used to demonstrate the advantages of the proposed method. It is shown that the proposed approach can greatly enhance the adaptability and the flexibility giving fully polarimetric SAR for terrain cover classification. The integration of fuzzy c-means (FCM) and fast generalization dynamic learning neural network (DLNN) capabilities makes the proposed algorithm an attractive and alternative method for polarimetric SAR classification. Numéro de notice : A2003-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.813494 En ligne : https://doi.org/10.1109/TGRS.2003.813494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22550 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible