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Spectral resolution requirements for mapping urban areas / Martin Herold in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)
[article]
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 classificationRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1907 - 1919[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-03091 RAB Revue Centre de documentation En réserve L003 Disponible Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness / M.E. Hodgson in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)
[article]
Titre : Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness Type de document : Article/Communication Auteurs : M.E. Hodgson, Auteur ; J.R. Jensen, Auteur ; J.A. Tullis, Auteur ; K.D. Riordan, Auteur ; C.M. Archer, Auteur Année de publication : 2003 Article en page(s) : pp 973 - 980 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] orthoimage
[Termes IGN] parcelle cadastrale
[Termes IGN] photographie en couleur
[Termes IGN] ruissellement
[Termes IGN] surface imperméable
[Termes IGN] système expertRésumé : (Auteur) The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (segments) were aggregated to parcels. The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0. 955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016). Numéro de notice : A2003-227 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.973 En ligne : https://doi.org/10.14358/PERS.69.9.973 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22522
in Photogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 973 - 980[article]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)
[article]
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] variogrammeRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1950 - 1958[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)
[article]
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] vectorisationRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 2089 - 2100[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-03091 RAB Revue Centre de documentation En réserve L003 Disponible Impact of topographic normalization on land-cover classification accuracy / S.R. Hale in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 7 (July 2003)
[article]
Titre : Impact of topographic normalization on land-cover classification accuracy Type de document : Article/Communication Auteurs : S.R. Hale, Auteur ; B.N. Rock, Auteur Année de publication : 2003 Article en page(s) : pp 785 - 791 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] image Landsat-ETM+
[Termes IGN] modèle numérique de terrain
[Termes IGN] occupation du sol
[Termes IGN] précision de la classificationRésumé : (Auteur) For pixel classifying algorithms to perform effectively, effects of topographic relief must be minimized or removed. In areas of high topographic relief, problems arise when spectral variations in ground target illumination and radiance, owing to differences in incident radiation and nonLambertian reflectance behavior, respectively, cause identical landcover types to reflect differently, or different cover types to reflect similarly. A Landsat Enhanced Thematic Mapper image was processed using band ratios, the Minnaert Correction, aspect partitioning, and combinations of these treatments to generate independent landcover classifications. Treatment classification accuracy was determined using error matrices and the Kappa statistic. Producer's and User's Accuracies were examined to determine if treatments were superior at producing greater class specific accuracy. None of the treatments produced a significantly more accurate classification; however, assessment of classspecific accuracies indicated accuracy gains using aspect partitioning alone or in combination with the Minnaert Correction. Numéro de notice : A2003-154 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.7.785 En ligne : https://doi.org/10.14358/PERS.69.7.785 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22450
in Photogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 7 (July 2003) . - pp 785 - 791[article]Land-cover change monitoring with classification trees using Landsat TM and ancillary data / J. Rogan in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 7 (July 2003)PermalinkEstimating local variations in land use statistics / A. Geddes in International journal of geographical information science IJGIS, vol 17 n° 4 (june 2003)PermalinkEvaluation of airborne video data for land-cover classification accuracy assessment / I.T. Grierson in Geocarto international, vol 18 n° 2 (June - August 2003)PermalinkEvaluation of land use and cover changes in North Shaanxi, China / W. Wu in Photo interprétation, vol 39 n° 2 (Juin 2003)PermalinkFusion 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)PermalinkMultipath mitigation: how good can it get with new signals ? / L.R. Weill in GPS world, vol 14 n° 6 (June 2003)PermalinkProcessing Hyperion and ALI for forest classification / D.G. Goodenough in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)PermalinkSatellite 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)PermalinkComparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image / B. Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 5 (May 2003)PermalinkImage calibration to like-values in mapping shallow water quality from multitemporal data / M.A.. Islam in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 5 (May 2003)Permalink