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Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery / T. Ainsworth in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 5 (September - October 2009)
[article]
Titre : Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery Type de document : Article/Communication Auteurs : T. Ainsworth, Auteur ; J. Kelly, Auteur ; J. Lee, Auteur Année de publication : 2009 Article en page(s) : pp 464 - 471 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] image radar moirée
[Termes IGN] modèle de rétrodiffusion
[Termes IGN] polarimétrie radar
[Termes IGN] polarisation
[Termes IGN] polarisation croisée
[Termes IGN] précision de la classificationRésumé : (Auteur) We present a study of the polarimetric information content of dual-pol imaging modes and dual-pol imaging extended by polarimetric scattering models. We compare Wishart classifications both among the partial polarimetric datasets and against the full quad-pol dataset. Our emphasis is the inter-comparisons between the classification results based on dual-pol modes, compact polarimetric modes and scattering model extensions of the compact polarimetric modes. We primarily consider novel dual-pol modes, e.g. transmitting a circular polarization and receiving horizontal and vertical polarizations, and the pseudo-quad-pol data derived from polarimetric scattering models based on dual-pol data. We show that the overall classification accuracy of the pseudo-quad-pol data is essential the same as the classification accuracy obtained directly employing the underlying dual-pol imagery.We present a study of the polarimetric information content of dual-pol imaging modes and dual-pol imaging extended by polarimetric scattering models. We compare Wishart classifications both among the partial polarimetric datasets and against the full quad-pol dataset. Our emphasis is the inter-comparisons between the classification results based on dual-pol modes, compact polarimetric modes and scattering model extensions of the compact polarimetric modes. We primarily consider novel dual-pol modes, e.g. transmitting a circular polarization and receiving horizontal and vertical polarizations, and the pseudo-quad-pol data derived from polarimetric scattering models based on dual-pol data. We show that the overall classification accuracy of the pseudo-quad-pol data is essential the same as the classification accuracy obtained directly employing the underlying dual-pol imagery. Copyright ISPRS Numéro de notice : A2009-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.12.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.12.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30030
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 5 (September - October 2009) . - pp 464 - 471[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09051 SL Revue Centre de documentation Revues en salle Disponible Developing collaborative classifiers using an Expert-based Model / Giorgos Mountrakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 7 (July 2009)
[article]
Titre : Developing collaborative classifiers using an Expert-based Model Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; R. Watts, Auteur ; L. Luo, Auteur ; Jing Wang, Auteur Année de publication : 2009 Article en page(s) : pp 831 - 843 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] classification à base de connaissances
[Termes IGN] image Landsat
[Termes IGN] Las Vegas
[Termes IGN] mise à l'échelle
[Termes IGN] précision de la classification
[Termes IGN] surface imperméable
[Termes IGN] système expertRésumé : (Auteur) This paper presents a hierarchical, multi-stage adaptive strategy for image classification. We iteratively apply various classification methods (e.g., decision trees, neural networks), identify regions of parametric and geographic space where accuracy is low, and in these regions, test and apply alternate methods repeating the process until the entire image is classified. Currently, classifiers are evaluated through human input using an expert-based system; therefore, this paper acts as the proof of concept for collaborative classifiers. Because we decompose the problem into smaller, more manageable sub-tasks, our classification exhibits increased flexibility compared to existing methods since classification methods are tailored to the idiosyncrasies of specific regions. A major benefit of our approach is its scalability and collaborative support since selected low-accuracy classifiers can be easily replaced with others without affecting classification accuracy in high accuracy areas. At each stage, we develop spatially explicit accuracy metrics that provide straightforward assessment of results by non-experts and point to areas that need algorithmic improvement or ancillary data. Our approach is demonstrated in the task of detecting impervious surface areas, an important indicator for human-induced alterations to the environment, using a 2001 Landsat scene from Las Vegas, Nevada. Copyright ASPRS Numéro de notice : A2009-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.7.831 En ligne : https://doi.org/10.14358/PERS.75.7.831 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29893
in Photogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 7 (July 2009) . - pp 831 - 843[article]Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)
[article]
Titre : Optimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis Type de document : Article/Communication Auteurs : L. Su, Auteur Année de publication : 2009 Article en page(s) : pp 407 - 413 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] carte de la végétation
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste marge
[Termes IGN] zone semi-arideRésumé : (Auteur) In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach. Copyright ISPRS Numéro de notice : A2009-297 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.02.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29927
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 4 (July - August 2009) . - pp 407 - 413[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09041 SL Revue Centre de documentation Revues en salle Disponible An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery / J. Chan in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)
[article]
Titre : An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery Type de document : Article/Communication Auteurs : J. Chan, Auteur ; R. Bellens, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 397 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification pixellaire
[Termes IGN] détail topographique artificiel
[Termes IGN] eCognition
[Termes IGN] image à très haute résolution
[Termes IGN] milieu urbain
[Termes IGN] objet géographique urbain
[Termes IGN] précision de la classification
[Termes IGN] traitement géométrique de donnéesRésumé : (Auteur) In this paper, we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes “roads and buildings with dark roof” after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition®, containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information. Copyright ASPRS Numéro de notice : A2009-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.397 En ligne : https://doi.org/10.14358/PERS.75.4.397 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29736
in Photogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 397 - 411[article]Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier / R. Philipps in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)
[article]
Titre : Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier Type de document : Article/Communication Auteurs : R. Philipps, Auteur ; L. Watson, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 107 - 116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse en composantes principales
[Termes IGN] classificateur paramétrique
[Termes IGN] classification hybride
[Termes IGN] décomposition d'image
[Termes IGN] image Landsat-ETM+
[Termes IGN] image multitemporelle
[Termes IGN] précision de la classification
[Termes IGN] Rondonia (Brésil)
[Termes IGN] Virginie (Etats-Unis)Résumé : (Auteur) Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This paper introduces a feature reduction method based on the singular value decomposition (SVD). This SVD-based feature reduction method reduces the storage and processing requirements of the SVD by utilizing a training dataset. This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondônia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/non-forest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVD-based feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondônia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVD-based feature reduction can produce statistically significantly better classifications than PCA. Copyright ISPRS Numéro de notice : A2009-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29660
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 1 (January - February 2009) . - pp 107 - 116[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09011 SL Revue Centre de documentation Revues en salle Disponible Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses / F. Aguera in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 6 (November - December 2008)PermalinkA standardized probability comparison approach for evaluating and combining pixel-based classification procedures / D. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 5 (May 2008)PermalinkArtificial immune-based supervised classifier for land-cover classification / M. Pal in International Journal of Remote Sensing IJRS, vol 29 n° 7 (April 2008)PermalinkLand-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network / H. Bagan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)PermalinkImproved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode / S.A. Soenen in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkBorder 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)PermalinkWeighting function alternatives for a subpixel allocation model / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)PermalinkAccuracy 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)PermalinkOptimizing image resolution to maximize the accuracy of hard classification / K.R. Mccloy in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)PermalinkComparative assessment of the measures of thematic classification accuracy / C. Liu in Remote sensing of environment, vol 107 n° 4 (30/04/2007)Permalink