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Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
[article]
Titre : Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images Type de document : Article/Communication Auteurs : N.G. Kasapoglu, Auteur ; O.K. Ersoy, Auteur Année de publication : 2007 Article en page(s) : pp 3880 - 3893 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] précision de la classificationRésumé : (Auteur) Effective partitioning of the feature space for high classification accuracy with due attention to rare class members is often a difficult task. In this paper, the border vector detection and adaptation (BVDA) algorithm is proposed for this purpose. The BVDA consists of two parts. In the first part of the algorithm, some specially selected training samples are assigned as initial reference vectors called border vectors. In the second part of the algorithm, the border vectors are adapted by moving them toward the decision boundaries. At the end of the adaptation process, the border vectors are finalized. The method next uses the minimum distance to border vector rule for classification. In supervised learning, the training process should be unbiased to reach more accurate results in testing. In the BVDA, decision region borders are related to the initialization of the border vectors and the input ordering of the training samples. Consensus strategy can be applied with cross validation to reduce these dependencies. The performance of the BVDA and consensual BVDA were studied in comparison to other classification algorithms including neural network with backpropagation learning, support vector machines, and some statistical classification techniques. Copyright IEEE Numéro de notice : A2007-582 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.900699 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4378538 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28945
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3880 - 3893[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A 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)
[article]
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)
[article]
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]Optimizing image resolution to maximize the accuracy of hard classification / K.R. Mccloy in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)
[article]
Titre : Optimizing image resolution to maximize the accuracy of hard classification Type de document : Article/Communication Auteurs : K.R. Mccloy, Auteur ; P.K. Bocher, Auteur Année de publication : 2007 Article en page(s) : pp 893 - 903 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] limite de résolution géométrique
[Termes IGN] matrice de confusion
[Termes IGN] précision de la classification
[Termes IGN] théorie des erreurs
[Termes IGN] varianceRésumé : (Auteur) There are three strategies by which the accuracy of classification can be improved after the imagery that will be used for the classification has been chosen. These are to improve the definition of the class decision surfaces, to maximize the between class distances, and to reduce the within class variances. This paper reports on work done to investigate the relationship between classification accuracy and within class variances, where generally accepted measures of accuracy derived from the Confusion Matrix are used as the indicators of classification accuracy. This paper shows that the within class variances are a function of image resolution, and it provides a mechanism based on the Average Local Variance (ALV) function to find the resolution that will yield the highest relative within field classification accuracy by minimizing the within class variances. Copyright ASPRS Numéro de notice : A2007-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.8.893 En ligne : http://dx.doi.org/10.14358/PERS.73.8.893 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28732
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 8 (August 2007) . - pp 893 - 903[article]Comparative assessment of the measures of thematic classification accuracy / C. Liu in Remote sensing of environment, vol 107 n° 4 (30/04/2007)
[article]
Titre : Comparative assessment of the measures of thematic classification accuracy Type de document : Article/Communication Auteurs : C. Liu, Auteur ; P. Frazier, Auteur ; L. Kumar, Auteur Année de publication : 2007 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] cartographie thématique
[Termes IGN] classificateur
[Termes IGN] coefficient de corrélation
[Termes IGN] cohérence des données
[Termes IGN] niveau d'analyse
[Termes IGN] précision de la classification
[Termes IGN] précision des données
[Termes IGN] producteur
[Termes IGN] utilisateurRésumé : (Auteur) Accuracy assessment of classified imagery is an important task in remote sensing. Various measures have been developed to describe and compare the accuracy of maps and the performance of different classifiers, but the extent to which these measures are consistent with each other is largely unknown. In this paper the consistency of fourteen category-level and twenty map-level accuracy measures was tested on 595 published error matrices using nonparametric correlation coefficients (Spearman's rho and Kendall's tau-b) as well as the probability of concordance. The results show that four groups can be identified for the category-level measures and three groups for map-level measures. The consistency among the measures within a group is generally higher than that among the measures from different groups though all the measures at the same level are highly consistent with each other. We recommend that user's accuracy and producer's accuracy and the overall accuracy should be provided as primary accuracy measures and the two relative entropy change measures and the mutual information normalized by the arithmetic mean of the entropies on map and ground truthing be provided as supplementary measures. The chance-corrected, error matrix-normalized and user's and producer's accuracy-combined measures were found to contain estimation and interpretation problems at both category- and map-levels and are therefore not recommended for general use. Copyright Elsevier Numéro de notice : A2007-158 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.10.010 En ligne : https://doi.org/10.1016/j.rse.2006.10.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28521
in Remote sensing of environment > vol 107 n° 4 (30/04/2007)[article]Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery / J. Pasher in Remote sensing of environment, vol 107 n° 3 (12 April 2007)PermalinkComparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)PermalinkTerrestrial and submerged aquatic vegetation mapping in Fire Island national seashore using high spatial resolution remote sensing data / Y. Wang in Marine geodesy, vol 30 n° 1-2 (March - June 2007)PermalinkExtraction of spectral channels from hyperspectral images for classification purposes / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)PermalinkAssessing the effect of attribute uncertainty on the robustness of choropleth map classification / N. Xiao in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)PermalinkComparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China / G. Yan in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)PermalinkLa transformation en ondelettes pour l'extraction de la texture-couleur : application à la classification combinée des images (HRV) de SPOT / A. Safia in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)PermalinkA pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery / L. Zhang in IEEE Transactions on geoscience and remote sensing, vol 44 n° 10 Tome 2 (October 2006)PermalinkIncorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach / A.E. Daniels in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkInterrelationships between spatial resolution and per-pixel classifiers for extracting information classes part 1: the urban environment / J.R. Jensen (29/03/2006)PermalinkParcel-based classification / J. Wijnant in GEO: Geoconnexion international, vol 5 n° 2 (february 2006)PermalinkEtude de différents facteurs influant les classifications d'images multi-résolution / F. Kazemipour (2006)PermalinkUsing satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed / X. Yang in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)PermalinkSpectral filtering and classification of terrestrial laser scanner point clouds / Derek D. Lichti in Photogrammetric record, vol 20 n° 111 (September - November 2005)PermalinkCombining spectral and spatial information into hidden Markov models for unsupervised image classification / B. Tso in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)PermalinkRepresenting and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)PermalinkA comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification / C.W. Emerson in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)PermalinkThe utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery / Anne Puissant in International Journal of Remote Sensing IJRS, vol 26 n° 4 (February 2005)PermalinkSegmentation and classification of airborne laser scanner data / George Sithole (2005)Permalink