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Combining 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)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2113 - 2133 Titre : Combining spectral and spatial information into hidden Markov models for unsupervised image classification Type de document : Article/Communication Auteurs : B. Tso, Auteur ; C. Olsen, Auteur Année de publication : 2005 Article en page(s) : pp 2113 - 2133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification barycentrique [Termes IGN] classification contextuelle [Termes IGN] classification non dirigée [Termes IGN] données localisées 2D [Termes IGN] image multi sources [Termes IGN] modèle de Markov [Termes IGN] optimisation (mathématiques) [Termes IGN] précision de la classification [Termes IGN] qualité des données Résumé : (Auteur) Unsupervised classification methodology applied to remote sensing image processing can provide benefits in automatically converting the raw image data into useful information so long as higher classification accuracy is achieved. The traditional k-means clustering scheme using spectral data alone does not perform well in general as far as accuracy is concerned. This is partly due to the failure to take the spatial inter-pixels dependencies (i.e. the context) into account, resulting in a 'busy' visual appearance to the output imagery. To address this, the hidden Markov models (HMM) are introduced in this study as a fundamental framework to incorporate both the spectral and contextual information in analysis. This helps generate more patch-like output imagery and produces higher classification accuracy in an unsupervised scheme. The newly developed unsupervised classification approach is based on observation-sequence and observation-density adjustments, which have been proposed for incorporating 2D spatial information into the linear HMM. For the observation-sequence adjustment methods, there are a total of five neighbourhood systems being proposed. Two neighbourhood systems were incorporated into the observation-density methods for study. The classification accuracy is then evaluated by means of confusion matrices made by randomly chosen test samples. The classification obtained by k-means clustering and the HMM with commonly seen strip-like and Hilbert-Peano sequence fitting methods were also measured. Experimental results showed that the proposed approaches for combining both the spectral and spatial information into HMM unsupervised classification mechanism present improvements in both classification accuracy and visual qualities. Numéro de notice : A2005-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331337844 En ligne : https://doi.org/10.1080/01431160512331337844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27395 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05101	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05101	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images / Nikhil R. Pal 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 2219 - 2240 Titre : Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images Type de document : Article/Communication Auteurs : Nikhil R. Pal, Auteur ; Arijit Laha, Auteur ; Jyotirmay Das, Auteur Année de publication : 2005 Article en page(s) : pp 2219 - 2240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de Kohonen [Termes IGN] classificateur [Termes IGN] classification floue [Termes IGN] image multibande [Termes IGN] raisonnement flou Résumé : (Auteur) We propose a novel scheme for designing fuzzy rule based classifiers. A selforganizing feature map (SOFM) based method is used for generating a set of prototypes, which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different contexts leading to context sensitive inferencing. To realize context sensitive inferencing we used a softmin operator with a tuneable parameter. The proposed scheme is tested on several multispectral satellite image datasets and the performance is found to be much better than the results reported in the literature. Numéro de notice : A2005-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500033419 En ligne : https://doi.org/10.1080/01431160500033419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27397 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05101	RAB	Revue	Centre de documentation	En réserve L003	Disponible