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Neural network model for standard PCA and its variants applied to remote sensing / S. Chitroub in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2197 - 2218 Titre : Neural network model for standard PCA and its variants applied to remote sensing Type de document : Article/Communication Auteurs : S. Chitroub, Auteur Année de publication : 2005 Article en page(s) : pp 2197 - 2218 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] extraction automatique [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] matrice de covariance [Termes IGN] modèle topologique réseau [Termes IGN] réseau neuronal artificiel [Termes IGN] valeur propre Résumé : (Auteur) The conventional approach for principal component analysis (PCA) and its variants applied to remote sensing involves the computation of the input data covariance/correlation matrix and/or that of noise and application of diagonalization procedures for extracting the eigenvalues and corresponding eigenvectors. When the data dimension grows significantly, the matrix computations and manipulations become practically inefficient and inaccurate due to round-off errors. In addition, all the eigenvalues and their corresponding eigenvectors have to be evaluated. These deficiencies make the conventional scheme inefficient for remote sensing applications. For that we propose here a neural network model that performs the PCA and its variants directly from the original data without any additional non-neuronal computations or preliminary matrix estimation. Since the end user is usually not a neural network specialist, the neural network model as well as its execution are carefully designed in order to be automated as much as possible. This includes both the design of the network topology and the input/output representation as well as the design of the training algorithms. The global convergence of the model is studied. Its application has been realized on Landsat Thematic Mapper (TM) multispectral data. The obtained results show that the model performs well. Numéro de notice : A2005-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500075899 En ligne : https://doi.org/10.1080/01431160500075899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27396 [article]

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