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A time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
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Titre : A time-efficient method for anomaly detection in hyperspectral images Type de document : Article/Communication Auteurs : O. Duran, Auteur ; M. Petrou, Auteur Année de publication : 2007 Article en page(s) : pp 3894 - 3918 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de Kohonen
[Termes IGN] classification ISODATA
[Termes IGN] détection d'anomalie
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) We propose a computationally efficient method for determining anomalies in hyperspectral data. In the first stage of the algorithm, the background classes, which are the dominant classes in the image, are found. The method consists of robust clustering of a randomly chosen small percentage of the image pixels. The clusters are the representatives of the background classes. By using a subset of the pixels instead of the whole image, the computation is sped up, and the probability of including outliers in the background model is reduced. Anomalous pixels are the pixels with spectra that have large relative distances from the cluster centers. Several clustering techniques are investigated, and experimental results using realistic hyperspectral data are presented. A self-organizing map clustered using the local minima of the U-matrix (unified distance matrix) is identified as the most reliable method for background class extraction. The proposed algorithm for anomaly detection is evaluated using realistic hyperspectral data, is compared with a state-of-the-art anomaly detection algorithm, and is shown to perform significantly better. Copyright IEEE Numéro de notice : A2007-583 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.909205 En ligne : https://doi.org/10.1109/TGRS.2007.909205 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28946
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3894 - 3918[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Sea-ice deformation state from synthetic aperture radar imagery: Part 1 comparison of C- and L-band and different polarization / W. Dierking in IEEE Transactions on geoscience and remote sensing, vol 45 n° 11 Tome 2 (November 2007)
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Titre : Sea-ice deformation state from synthetic aperture radar imagery: Part 1 comparison of C- and L-band and different polarization Type de document : Article/Communication Auteurs : W. Dierking, Auteur ; J. Dall, Auteur Année de publication : 2007 Article en page(s) : pp 3610 - 3622 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] classification dirigée
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radar
[Termes IGN] polarisationRésumé : (Auteur) In this paper, we present a quantitative comparison of L- and C-band airborne synthetic aperture radar imagery acquired at like- and cross-polarizations over deformed sea ice under winter conditions. The parameters characterizing the deformation state of the ice are determined at both radar bands and at different polarizations. The separation of deformed and level ice is based on a target detection technique. The threshold is set such that image pixels with intensities equal to or larger than the highest 2% of the level-ice intensity distribution are classified as deformed ice, independent of the radar configuration and ice conditions. Optical imagery of sufficient quality for comparison is available only in a very few cases. To characterize the deformation state, the areal fraction of deformation features and the average distance between these features are evaluated. The values obtained for both parameters are very sensitive to the radar frequency. Aeral fractions are larger, and average distances are smaller at L-band than at C-band because of the much higher intensity contrast between the deformed and level ice at L-band. The differences between polarizations at one radar band are smaller but not always negligible. In comparison to optical images, it was observed that deformed-ice areas can be distinguished from level ice over their whole length and exten-sion at L-band, whereas at C-band, often, only prominent parts are visible. Numéro de notice : A2007-508 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.903711 En ligne : https://doi.org/10.1109/TGRS.2007.903711 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28871
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 11 Tome 2 (November 2007) . - pp 3610 - 3622[article] Voir aussiExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07111B RAB Revue Centre de documentation En réserve L003 Disponible Weighting function alternatives for a subpixel allocation model / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)
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Titre : Weighting function alternatives for a subpixel allocation model Type de document : Article/Communication Auteurs : Y. Makido, Auteur ; A. Shortridge, Auteur Année de publication : 2007 Article en page(s) : pp 1233 - 1240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] allocation
[Termes IGN] analyse infrapixellaire
[Termes IGN] autocorrélation spatiale
[Termes IGN] classification barycentrique
[Termes IGN] image Ikonos
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] optimisation (mathématiques)
[Termes IGN] précision de la classificationRésumé : (Auteur) This study investigates the “pixel-swapping” optimization algorithm proposed by Atkinson for predicting subpixel land- cover distribution. Two limitations of this method are assessed: the arbitrary spatial range value and the arbitrary exponential model for characterizing spatial autocorrelation. Various alternative weighting functions are evaluated. For this assessment, two different simulation models are employed to develop spatially autocorrelated binary class raster maps. These rasters are then resampled to generate sets of representative medium-resolution class maps. Prior to conducting the subpixel allocation, the relationship between cell resolution and spatial autocorrelation, as measured by Moran’s I, is evaluated. It is discovered that the form of this relationship depends upon the simulation model. For all tested weighting functions (Nearest Neighbor, Gaussian, Exponential, and IDW), the pixel swapping method increased classification accuracy compared with the initial random allocation of subpixels. Nearest Neighbor allocation performs as well as the more complex models of spatial structure. Copyright ASPRS Numéro de notice : A2007-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.11.1233 En ligne : http://dx.doi.org/10.14358/PERS.73.11.1233 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28877
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 11 (November 2007) . - pp 1233 - 1240[article]Accuracy of forest mapping based on Landsat TM data and a kNN-based method / K. Gjertsen in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
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Titre : Accuracy of forest mapping based on Landsat TM data and a kNN-based method Type de document : Article/Communication Auteurs : K. Gjertsen, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 420 - 430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] données multisources
[Termes IGN] forêt
[Termes IGN] image Landsat-TM
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Norvège
[Termes IGN] précision de la classificationRésumé : (Auteur) A multi-source forest inventory (MSFI) method has been developed for use in the Norwegian National Forest Inventory (NFI). The method is based on a k-nearest neighbour rule and uses field plots from the NFI, land cover maps, and satellite image data from Landsat Thematic Mapper. The inventory method is used to produce maps of selected forest variables and to estimate the selected forest variables for large areas such as municipalities. In this study, focus has been on the qualitative variables ‘dominating species group’ and ‘development class’ because these variables are of central interest to forest managers. A mid-summer Landsat 5 TM scene was used as image data, and all NFI plots inside the scene were used as a reference dataset. The relationship between the spectral bands and the forest variables was analysed, and it was found that the levels of association were low. A leave-one-out method based on the reference dataset was used to estimate the pixel-level accuracies. They were found to be relatively low with 63% agreement for species groups. An independent control survey was available for a municipality and estimates from the MSFI were compared to it. The levels of error were quite high. It was concluded that the large area estimates were biased by the reference dataset. Copyright Elsevier Numéro de notice : A2007-410 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.018 En ligne : https://doi.org/10.1016/j.rse.2006.08.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28773
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 420 - 430[article]Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
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Titre : Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium Type de document : Article/Communication Auteurs : F.M.B. Van Coillie, Auteur ; L.P.C. Verbeke, Auteur ; R.R. DE Wulf, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 476 - 487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] Flandre (Belgique)
[Termes IGN] forêt tempérée
[Termes IGN] image Ikonos
[Termes IGN] segmentation d'imageRésumé : (Auteur) Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p Numéro de notice : A2007-412 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.03.020 En ligne : https://doi.org/10.1016/j.rse.2007.03.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28775
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 476 - 487[article]vol 110 n° 4 - 30/10/2007 - Forestsat 2007 (Bulletin de Remote sensing of environment) / Ronald E. McRoberts
PermalinkThe impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes / T. Koukal in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
PermalinkClassification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data / G.W. Geerling in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
PermalinkClassified road detection from satellite images based on perceptual organization / J. Yang in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
PermalinkA rough set approach to the discovery of classification rules in spatial data / Yee Leung in International journal of geographical information science IJGIS, vol 21 n° 9-10 (october 2007)
PermalinkCharacterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)
PermalinkCarte de consensualité / A. Quirin in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)
PermalinkCartographie des zones de haute montagne : essais de cartographie numérique des rochers / Loïc Gondol in Le monde des cartes, n° 193 (septembre - novembre 2007)
PermalinkDetection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)
PermalinkEstimation of vegetation parameter for modelling soil erosion using linear spectral mixture analysis of Landsat ETM data / A.M. DE Asis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)
PermalinkA geographic visualization approach to multi-criteria evaluation of urban quality of life / Claus Rinner in International journal of geographical information science IJGIS, vol 21 n° 8 (september 2007)
PermalinkHigher order vagueness in geographical information: empirical geographical population of type N fuzzy sets / P. Fischer in Geoinformatica, vol 11 n° 3 (September - November 2007)
PermalinkLa morphologie mathématique binaire pour l'extraction automatique des bâtiments dans les images THRS / David Sheeren in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)
PermalinkA new model for cloud tracking and analysis on satellite images / E. Guilbert in Geoinformatica, vol 11 n° 3 (September - November 2007)
PermalinkMultitemporel fuzzy classification model based on class transition possibilities / G.L.A. Mota in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 3 (August 2007)
PermalinkRule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping / Robert Lucas in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 3 (August 2007)
PermalinkDétection des haies et segmentation automatique / A. Dommanget in Géomatique expert, n° 57 (01/07/2007)
PermalinkAlgorithms for nearest neighbor search on moving object trajectories / E. Frentzos in Geoinformatica, vol 11 n° 2 (June - August 2007)
PermalinkBuilding detection by fusion of airborne laser scanner data and multi-spectral images: performance evaluation and sensitivity analysis / Franz Rottensteiner in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)
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