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An adaptive thresholding multiple classifiers system for remote sensing image classification / Y. Tzeng in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 6 (June 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 6 (June 2009) . - pp 679 - 687 Titre : An adaptive thresholding multiple classifiers system for remote sensing image classification Type de document : Article/Communication Auteurs : Y. Tzeng, Auteur ; K. Fan, Auteur ; K.S. Chen, Auteur Année de publication : 2009 Article en page(s) : pp 679 - 687 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classificateur [Termes IGN] classification automatique [Termes IGN] classification hybride [Termes IGN] ensachage [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] seuillage d'image Résumé : (Auteur) A multiple classifiers system which adopts an effective weighting policy to combine the output of several classifiers, generally leads to a better performance in image classification. The two most commonly used weighting policies are Bagging and Boosting algorithms. However, their performance is limited by high levels of ambiguity among classes. To overcome this difficulty, an adaptive thresholding criterion was proposed. By applying it to SAR and optical images for terrain cover classification, comparisons between the multiple classifiers systems using the Bagging and/or Boosting algorithms with and without the adaptive thresholding criterion were made. Experimental results showed that the classification substantially improved when the adaptive thresholding criterion was used, especially when the level of ambiguity of targets was high. Copyright ASPRS Numéro de notice : A2009-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.6.679 En ligne : https://doi.org/10.14358/PERS.75.6.679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29890 [article]

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]

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A study of the validity of the integral equation model by moment method simulation : cylindrical case / M.F. Chen in Remote sensing of environment, vol 29 n° 3 (01/09/1989)  

Public [article]  inRemote sensing of environment > vol 29 n° 3 (01/09/1989) . - pp 217 - 228 Titre : A study of the validity of the integral equation model by moment method simulation : cylindrical case Type de document : Article/Communication Auteurs : M.F. Chen, Auteur ; K.S. Chen, Auteur ; A.K. Fung, Auteur Année de publication : 1989 Article en page(s) : pp 217 - 228 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique [Termes IGN] corrélation [Termes IGN] équation intégrale [Termes IGN] fréquence [Termes IGN] rétrodiffusion [Termes IGN] simulation Numéro de notice : A1989-440 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/0034-4257(89)90001-1 En ligne : https://doi.org/10.1016/0034-4257(89)90001-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25398 [article]